23.2.2.2 Object Based Land Cover, Parcels, Region Based Land Cover, Land Use Analysis

Yu, Q.[Qian], Gong, P.[Peng], Clinton, N.[Nick], Biging, G.[Greg], Kelly, M.[Maggi], Schirokauer, D.[Dave],
Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery,
PhEngRS(72), No. 7, July 2006, pp. 799-812.
WWW Link. 0610
Object-based classification applied in vegetation mapping at alliance level with 1-meter resolution airborne imagery compared with conventional pixel-based classification. BibRef

Yu, Q.[Qian], Gong, P.[Peng], Tian, Y.Q.[Yong Q.], Pu, R.L.[Rui-Liang], Yang, J.[Jun],
Factors Affecting Spatial Variation of Classification Uncertainty in an Image Object-based Vegetation Mapping,
PhEngRS(74), No. 8, August 2008, pp. 1007-1018.
WWW Link. 0804
A mixed linear model to examine the effect of six categories of factors on classification uncertainty in an object-based vegetation mapping, including general membership, topography, sample object density, spatial composition, sample object reliability and object features. BibRef

Rizvi, I.A., Mohan, B.K.,
Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process,
GeoRS(49), No. 12, December 2011, pp. 4815-4820.
IEEE DOI 1201
Classification of objects, not just pixels. BibRef

Xie, H.[Huan], Heipke, C.[Christian], Lohmann, P.[Peter], Soergel, U.[Uwe], Tong, X.H.[Xiao-Hua], Shi, W.Z.[Wen-Zhong],
A New Binary Encoding Algorithm for the Simultaneous Region-based Classification of Hyperspectral Data and Digital Surface Models,
PFG(2011), No. 1, 2011, pp. 17-33.
WWW Link. 1211
BibRef

Huth, J., Kuenzer, C., Wehrmann, T., Gebhardt, S., Tuan, V., Dech, S.,
Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification,
RS(4), No. 9, September 2012, pp. 2530-2553.
DOI Link 1210
BibRef

Wolf, N.[Nils],
Feature Evaluation for a Transferable Approach of Object-based Land Cover Classification Based on Ikonos and QuickBird Satellite Data,
PFG(2011), No. 3, 2011, pp. 135-144.
WWW Link. 1211
BibRef

Wolf, N.[Nils],
Object Features for Pixel-based Classi cation of Urban Areas Comparing Different Machine Learning Algorithms,
PFG(2013), No. 3, 2013, pp. 149-161.
DOI Link 1306
BibRef

Tang, H., Shen, L., Qi, Y., Chen, Y., Shu, Y., Li, J., Clausi, D.A.,
A Multiscale Latent Dirichlet Allocation Model for Object-Oriented Clustering of VHR Panchromatic Satellite Images,
GeoRS(51), No. 3, March 2013, pp. 1680-1692.
IEEE DOI 1303
semantic clustering of geo-objects. BibRef

Hu, Q.[Qiong], Wu, W.B.[Wen-Bin], Xia, T.[Tian], Yu, Q.Y.[Qiang-Yi], Yang, P.[Peng], Li, Z.G.[Zheng-Guo], Song, Q.[Qian],
Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping,
RS(5), No. 11, 2013, pp. 6026-6042.
DOI Link 1312
BibRef

Wang, M.[Min], Cui, Q.[Qi], Wang, J.[Jie], Ming, D.P.[Dong-Ping], Lv, G.N.[Guo-Nian],
Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features,
PandRS(123), No. 1, 2017, pp. 104-113.
Elsevier DOI 1612
Region-line primitive association framework. Separate from water. BibRef

Lv, Z.Y.[Zhi-Yong], Shi, W.Z.[Wen-Zhong], Zhou, X.C.[Xiao-Cheng], Benediktsson, J.A.[Jón Atli],
Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Zhang, X.K.[Xiao-Kang], Shi, W.Z.[Wen-Zhong], Lv, Z.Y.[Zhi-Yong], Peng, F.F.[Fei-Fei],
Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan-Vese Model,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Lv, Z.Y.[Zhi-Yong], Liu, T.F.[Tong-Fei], Zhang, P.L.[Peng-Lin], Benediktsson, J.A.[Jón Atli], Chen, Y.X.[Yi-Xiang],
Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Chen, X.[Xi], Zhou, G.J.[Gong-Jian], Chen, Y.S.[Yu-Shi], Shao, G.F.[Guo-Fan], Gu, Y.F.[Yan-Feng],
Supervised Multiview Feature Selection Exploring Homogeneity and Heterogeneity With L_1,2-Norm and Automatic View Generation,
GeoRS(55), No. 4, April 2017, pp. 2074-2088.
IEEE DOI 1704
feature selection BibRef

Gu, H.Y.[Hai-Yan], Li, H.T.[Hai-Tao], Yan, L.[Li], Liu, Z.J.[Zheng-Jun], Blaschke, T.[Thomas], Soergel, U.[Uwe],
An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Yang, B.[Bin], Liu, Z.J.[Zheng-Jun], Xing, Y.[Ying], Luo, C.F.[Cheng-Feng],
Remote Sensing Image Classification Based on Improved BP Neural Network,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

Gerçek, D.[Deniz],
A Conceptual Model for Delineating Land Management Units (LMUs) Using Geographical Object-Based Image Analysis,
IJGI(6), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Gerçek, D.[Deniz], Zeydanl, U.[Ugur],
Object-Based Classification of Landscape into Land Management Units (LMUs),
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Xu, Y.[Yan], Du, Q.[Qian], Li, W.[Wei], Chen, C.[Chen], Younan, N.H.[Nicolas H.],
Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Zhang, X.L.[Xue-Liang], Xiao, P.F.[Peng-Feng], Feng, X.Z.[Xue-Zhi],
Toward combining thematic information with hierarchical multiscale segmentations using tree Markov random field model,
PandRS(131), No. 1, 2017, pp. 134-146.
Elsevier DOI 1709
High-spatial resolution remote sensing image BibRef

Cheng, X.J.[Xiao-Juan], Lu, J.W.[Ji-Wen], Feng, J.J.[Jian-Jiang], Yuan, B.[Bo], Zhou, J.[Jie],
Scene recognition with objectness,
PR(74), No. 1, 2018, pp. 474-487.
Elsevier DOI 1711
Scene recognition BibRef

Huang, Y.H.[Yao-Huan], Zhao, C.P.[Chuan-Peng], Yang, H.J.[Hai-Jun], Song, X.Y.[Xiao-Yang], Chen, J.[Jie], Li, Z.H.[Zhong-Hua],
Feature Selection Solution with High Dimensionality and Low-Sample Size for Land Cover Classification in Object-Based Image Analysis,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Balasubramaniam, R., Namboodiri, S., Nidamanuri, R.R., Gorthi, R.K.S.S.[R. K. S. S.],
Active Learning-Based Optimized Training Library Generation for Object-Oriented Image Classification,
GeoRS(56), No. 1, January 2018, pp. 575-585.
IEEE DOI 1801
Databases, Detectors, Entropy, Hyperspectral imaging, Libraries, Training, Active learning (AL), multiclassifier systems, training library BibRef

Yu, L.L.[Long-Long], Su, J.H.[Jin-He], Li, C.[Chun], Wang, L.[Le], Luo, Z.[Ze], Yan, B.P.[Bao-Ping],
Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Georganos, S.[Stefanos], Lennert, M.[Moritz], Grippa, T.[Tais], Vanhuysse, S.[Sabine], Johnson, B.[Brian], Wolff, E.[Eléonore],
Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Gong, X.[Xi], Xie, Z.[Zhong], Liu, Y.Y.[Yuan-Yuan], Shi, X.G.[Xu-Guo], Zheng, Z.[Zhuo],
Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
Find higher-level features for low quality data. BibRef

Liu, T.[Tao], Abd-Elrahman, A.[Amr],
An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Qiu, S.H.[Shao-Hua], Wen, G.J.[Gong-Jian], Liu, J.[Jia], Deng, Z.P.[Zhi-Peng], Fan, Y.X.[Ya-Xiang],
Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Tang, Y.W.[Yun-Wei], Zhang, J.X.[Jing-Xiong], Jing, L.H.[Lin-Hai], Gao, H.[Han],
Geostatistical modelling of spatial dependence in area-class occurrences for improved object-based classifications of remote-sensing images,
PandRS(141), 2018, pp. 219-236.
Elsevier DOI 1806
GEOBIA, Geostatistics, Spatial structural modelling, Change-of-support, Area-class, Image segmentation, Image classification BibRef

Zeng, D.[Dan], Chen, S.J.[Shuai-Jun], Chen, B.Y.[Bo-Yang], Li, S.Y.[Shu-Ying],
Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Li, P.[Peng], Ren, P.[Peng], Zhang, X.Y.[Xiao-Yu], Wang, Q.[Qian], Zhu, X.O.[Xia-Obin], Wang, L.[Lei],
Region-Wise Deep Feature Representation for Remote Sensing Images,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Arief, H.A.[Hasan Asyari], Strand, G.H.[Geir-Harald], Tveite, H.[Håvard], Indahl, U.G.[Ulf Geir],
Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zanotta, D.C.[Daniel Capella], Zortea, M.[Maciel], Ferreira, M.P.[Matheus Pinheiro],
A supervised approach for simultaneous segmentation and classification of remote sensing images,
PandRS(142), 2018, pp. 162-173.
Elsevier DOI 1807
Object-based image analysis, Segmentation, Supervised classification, Multispectral imaging BibRef

Zhu, P.P.[Pan-Pan], Zhang, L.Q.[Li-Qiang], Wang, Y.B.[Yue-Bin], Mei, J.[Jie], Zhou, G.Q.[Guo-Qing], Liu, F.Y.[Fang-Yu], Liu, W.W.[Wei-Wei], Mathiopoulos, P.T.[P. Takis],
Projection learning with local and global consistency constraints for scene classification,
PandRS(144), 2018, pp. 202-216.
Elsevier DOI 1809
Scene classification, Projection learning, Manifold regularization, Label consistency, Classification error BibRef

Liu, S.[Shuo], Ding, W.R.[Wen-Rui], Liu, C.H.[Chun-Hui], Liu, Y.[Yu], Wang, Y.F.[Yu-Feng], Li, H.G.[Hong-Guang],
ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Merciol, F.[François], Faucqueur, L.[Loïc], Damodaran, B.B.[Bharath Bhushan], Rémy, P.Y.[Pierre-Yves], Desclée, B.[Baudouin], Dazin, F.[Fabrice], Lefèvre, S.[Sébastien], Masse, A.[Antoine], Sannier, C.[Christophe],
GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
BibRef

Merciol, F.[François], Pham, M.T., Maia, D.S.[D. Santana], Masse, A., Sannier, C.,
Broceliande: A Comparative Study of Attribute Profiles and Feature Profiles From Different Attributes,
ISPRS20(B3:1371-1377).
DOI Link 2012
BibRef

Song, H.[Hunsoo], Kim, Y.H.[Yong-Hyun], Kim, Y.I.[Yong-Il],
A Patch-Based Light Convolutional Neural Network for Land-Cover Mapping Using Landsat-8 Images,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Radoux, J.[Julien], Bourdouxhe, A.[Axel], Coos, W.[William], Dufrêne, M.[Marc], Defourny, P.[Pierre],
Improving Ecotope Segmentation by Combining Topographic and Spectral Data,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Dutta, P.K.,
Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis,
IJIG(19), No. 1 2018, pp. 1950002.
DOI Link 1902
BibRef

Yang, L.B.[Ling-Bo], Mansaray, L.R.[Lamin R.], Huang, J.F.[Jing-Feng], Wang, L.M.[Li-Min],
Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Rajbhandari, S.[Sachit], Aryal, J.[Jagannath], Osborn, J.[Jon], Lucieer, A.[Arko], Musk, R.[Robert],
Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Josselin, D.[Didier], Louvet, R.[Romain],
Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Hossain, M.D.[Mohammad D.], Chen, D.M.[Dong-Mei],
Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective,
PandRS(150), 2019, pp. 115-134.
Elsevier DOI 1903
OBIA, Remote sensing, High spatial resolution, Image segmentation, Geographic object BibRef

Lefèvre, S.[Sébastien], Sheeren, D.[David], Tasar, O.[Onur],
A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Zhang, X.D.[Xiao-Dong], Zhu, K.[Kun], Chen, G.Z.[Guan-Zhou], Tan, X.L.[Xiao-Liang], Zhang, L.F.[Li-Fei], Dai, F.[Fan], Liao, P.[Puyun], Gong, Y.F.[Yuan-Fu],
Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Wang, Q.[Qing], Li, M.Q.[Meng-Qi], Li, G.Q.[Gong-Quan], Zhang, J.L.[Ji-Ling], Yan, S.Y.[Shuo-Yue], Chen, Z.R.[Zhuo-Ran], Zhang, X.D.[Xiao-Dong], Chen, G.Z.[Guan-Zhou],
High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net,
RS(15), No. 14, 2023, pp. 3517.
DOI Link 2307
BibRef

Zhang, X.[Xiao], Liu, L.Y.[Liang-Yun], Chen, X.D.[Xi-Dong], Xie, S.[Shuai], Gao, Y.[Yuan],
Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Zhong, Y.F.[Yan-Fei], Luo, C.[Chang], Hu, X.[Xin], Wei, L.F.[Li-Fei], Wang, X.Y.[Xin-Yu], Jin, S.Y.[Shu-Ying],
Cropland Product Fusion Method Based on the Overall Consistency Difference: A Case Study of China,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
Current methods are not always consistent. BibRef

Cheng, W.S.[Wen-Sheng], Yang, W.[Wen], Wang, M.[Min], Wang, G.[Gang], Chen, J.Y.[Jin-Yong],
Context Aggregation Network for Semantic Labeling in Aerial Images,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Cheng, W.S.[Wen-Sheng], Yang, W.[Wen], Pan, Y., Guo, H., Cheng, Y.,
Context Aggregation Network for Semantic Labeling in Aerial Images 2,
ICIP19(4484-4488)
IEEE DOI 1910
Semantic Labeling, Convolutional Neural Networks, Context, Attention, Aerial Images BibRef

Zhou, Y.[Ya'nan], Luo, J.C.[Jian-Cheng], Feng, L.[Li], Zhou, X.C.[Xiao-Cheng],
DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Maxwell, A.E.[Aaron E.], Strager, M.P.[Michael P.], Warner, T.A.[Timothy A.], Ramezan, C.A.[Christopher A.], Morgan, A.N.[Alice N.], Pauley, C.E.[Cameron E.],
Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Wu, X.[Xin], Hong, D.F.[Dan-Feng], Tian, J.J.[Jiao-Jiao], Chanussot, J.[Jocelyn], Li, W.[Wei], Tao, R.[Ran],
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features,
GeoRS(57), No. 7, July 2019, pp. 5146-5158.
IEEE DOI 1907
Feature extraction, Remote sensing, Object detection, Optical imaging, Optical sensors, Detectors, Object detection, spatial-frequency domains BibRef

Liu, Y.[Yu], Yuan, Y.H.[Yi-Hong], Gao, S.[Song],
Modeling the Vagueness of Areal Geographic Objects: A Categorization System,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Dyson, J.[Jack], Mancini, A.[Adriano], Frontoni, E.[Emanuele], Zingaretti, P.[Primo],
Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Li, L.F.[Lian-Fa],
Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Al-Alimi, D.[Dalal], Shao, Y.X.[Yu-Xiang], Feng, R.[Ruyi], Al-Qaness, M.A.A.[Mohammed A. A.], Abd Elaziz, M.[Mohamed], Kim, S.[Sunghwan],
Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Al-Alimi, D.[Dalal], Al-Qaness, M.A.A.[Mohammed A. A.], Cai, Z.H.[Zhi-Hua], Dahou, A.[Abdelghani], Shao, Y.X.[Yu-Xiang], Issaka, S.[Sakinatu],
Meta-Learner Hybrid Models to Classify Hyperspectral Images,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, J.[Jun], Jiang, L.[Lili], Wang, Y.J.[Yong-Ji], Qi, Q.W.[Qing-Wen],
An Improved Hybrid Segmentation Method for Remote Sensing Images,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Liu, M.[Miao], Yu, T.[Tao], Gu, X.F.[Xing-Fa], Sun, Z.S.[Zhen-Sheng], Yang, J.[Jian], Zhang, Z.W.[Zhou-Wei], Mi, X.F.[Xiao-Fei], Cao, W.J.[Wei-Jia], Li, J.[Juan],
The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Lee, C.[Cheonjae], de Vries, W.T.[Walter Timo],
Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Lu, X., Zhang, Y., Yuan, Y., Feng, Y.,
Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection,
GeoRS(58), No. 1, January 2020, pp. 179-192.
IEEE DOI 2001
Object detection, Feature extraction, Remote sensing, Deep learning, Detectors, Logic gates, Semantics, Deep learning, small object detection BibRef

Hou, B., Ren, Z., Zhao, W., Wu, Q., Jiao, L.,
Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching,
GeoRS(58), No. 2, February 2020, pp. 956-970.
IEEE DOI 2001
Object detection, Training, Feature extraction, Proposals, Remote sensing, Testing, Predictive models, Object detection, spatial template matching BibRef

Qian, X.L.[Xiao-Liang], Lin, S.[Sheng], Cheng, G.[Gong], Yao, X.[Xiwen], Ren, H.L.[Hang-Li], Wang, W.[Wei],
Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Yu, Y.T.[Yong-Tao], Guan, H.Y.[Hai-Yan], Li, D.L.[Di-Long], Gu, T.N.[Tian-Nan], Tang, E., Li, A.[Aixia],
Orientation guided anchoring for geospatial object detection from remote sensing imagery,
PandRS(160), 2020, pp. 67-82.
Elsevier DOI 2001
Object detection, Orientation guided anchoring, Oriented anchor, Region proposal network, Remote sensing imagery BibRef

Derksen, D.[Dawa], Inglada, J.[Jordi], Michel, J.[Julien],
Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Su, T.F.[Teng-Fei], Zhang, S.W.[Sheng-Wei], Liu, T.X.[Ting-Xi],
Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Wang, H.[Hua], Li, W.W.[Wen-Wen], Huang, W.[Wei], Nie, K.[Ke],
A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Hoeser, T.[Thorsten], Kuenzer, C.[Claudia],
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Hoeser, T.[Thorsten], Bachofer, F.[Felix], Kuenzer, C.[Claudia],
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review: Part II: Applications,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Chmielewski, S.[Szymon], Bochniak, A.[Andrzej], Natapov, A.[Asya], Wezyk, P.[Piotr],
Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve Kózki, Poland,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Shin, S.J.[Su-Jin], Kim, S.[Seyeob], Kim, Y.[Youngjung], Kim, S.[Sungho],
Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Martins, V.S.[Vitor S.], Kaleita, A.L.[Amy L.], Gelder, B.K.[Brian K.], da Silveira, H.L.F.[Hilton L.F.], Abe, C.A.[Camila A.],
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution,
PandRS(168), 2020, pp. 56-73.
Elsevier DOI 2009
Deep learning, Convolutional neural network, Land cover, Aerial imagery BibRef

Liu, B.[Bo], Du, S.H.[Shi-Hong], Du, S.J.[Shou-Ji], Zhang, X.Y.[Xiu-Yuan],
Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Tran, B.H.[Ba-Huy], Aussenac-Gilles, N.[Nathalie], Comparot, C.[Catherine], Trojahn, C.[Cassia],
Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link 2009
Raster to properties. BibRef

Tran, B.H.[Ba-Huy], Aussenac-Gilles, N.[Nathalie], Comparot, C.[Catherine], Trojahn, C.[Cassia],
Semantic Integration of Raster Data for Earth Observation on Territorial Units,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Tang, Y.W.[Yun-Wei], Qiu, F.[Fang], Jing, L.H.[Lin-Hai], Shi, F.[Fan], Li, X.[Xiao],
Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches,
PandRS(169), 2020, pp. 320-336.
Elsevier DOI 2011
Object-based image analysis, Curve matching, His-Cov, Spatial covariogram, Land cover/land use classification, High resolution BibRef

Liu, W.[Wei], Wang, J.[Jian], Luo, J.C.[Jian-Cheng], Wu, Z.F.[Zhi-Feng], Chen, J.D.[Jing-Dong], Zhou, Y.[Yanan], Sun, Y.W.[Ying-Wei], Shen, Z.F.[Zhan-Feng], Xu, N.[Nan], Yang, Y.P.[Ying-Pin],
Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Shivappriya, S.N., Priyadarsini, M.J.P.[M. Jasmine Pemeena], Stateczny, A.[Andrzej], Puttamadappa, C., Parameshachari, B.D.,
Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
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Cao, Z.Y.[Zhi-Ying], Diao, W.H.[Wen-Hui], Sun, X.[Xian], Lyu, X.D.[Xiao-De], Yan, M.L.[Meng-Long], Fu, K.[Kun],
C3Net: Cross-Modal Feature Recalibrated, Cross-Scale Semantic Aggregated and Compact Network for Semantic Segmentation of Multi-Modal High-Resolution Aerial Images,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhang, W.[Wei], Tang, P.[Ping], Corpetti, T.[Thomas], Zhao, L.J.[Li-Jun],
WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Qu, L.[Le'an], Chen, Z.J.[Zhen-Jie], Li, M.C.[Man-Chun], Zhi, J.J.[Jun-Jun], Wang, H.M.[Hui-Ming],
Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Luo, C.[Chong], Qi, B.S.[Bei-Song], Liu, H.J.[Huan-Jun], Guo, D.[Dong], Lu, L.P.[Lv-Ping], Fu, Q.[Qiang], Shao, Y.Q.[Yi-Qun],
Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Xue, Y.A.[Yong-An], Zhao, J.L.[Jin-Ling], Zhang, M.M.[Ming-Mei],
A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
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Hong, R.[Rokgi], Park, J.[Jinseok], Jang, S.J.[Seong-Ju], Shin, H.J.[Hyung-Jin], Kim, H.[Hakkwan], Song, I.[Inhong],
Development of a Parcel-Level Land Boundary Extraction Algorithm for Aerial Imagery of Regularly Arranged Agricultural Areas,
RS(13), No. 6, 2021, pp. xx-yy.
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Li, W.[Wei], Wei, W.[Wei], Zhang, L.[Lei],
GSDet: Object Detection in Aerial Images Based on Scale Reasoning,
IP(30), 2021, pp. 4599-4609.
IEEE DOI 2105
Object detection, Feature extraction, Proposals, Convolution, Knowledge engineering, Detectors, Cognition, Object detection, reasoning BibRef

Yang, C.[Chun], Rottensteiner, F.[Franz], Heipke, C.[Christian],
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases,
PandRS(177), 2021, pp. 38-56.
Elsevier DOI 2106
Hierarchical consistent land use classification, CNN, Geospatial database, Aerial imagery BibRef

Tassi, A.[Andrea], Gigante, D.[Daniela], Modica, G.[Giuseppe], di Martino, L.[Luciano], Vizzari, M.[Marco],
Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Dong, R.[Ruchan], Jiao, L.C.[Li-Cheng], Zhang, Y.[Yan], Zhao, J.[Jin], Shen, W.Y.[Wei-Yan],
A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
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Xu, Z.Y.[Zhi-Yong], Zhang, W.C.[Wei-Cun], Zhang, T.X.[Tian-Xiang], Yang, Z.F.[Zhi-Fang], Li, J.Y.[Jiang-Yun],
Efficient Transformer for Remote Sensing Image Segmentation,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
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Wang, H.[Hong], Chen, X.Z.[Xian-Zhong], Zhang, T.X.[Tian-Xiang], Xu, Z.Y.[Zhi-Yong], Li, J.Y.[Jiang-Yun],
CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Cheng, X.[Xu], Liu, L.H.[Li-Hua], Song, C.[Chen],
A Cyclic Information-Interaction Model for Remote Sensing Image Segmentation,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Pare, S.[Shreya], Mittal, H.[Himanshu], Sajid, M.[Mohammad], Bansal, J.C.[Jagdish Chand], Saxena, A.[Amit], Jan, T.[Tony], Pedrycz, W.[Witold], Prasad, M.[Mukesh],
Remote Sensing Imagery Segmentation: A Hybrid Approach,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
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Wang, G.Q.[Guan-Qun], Zhuang, Y.[Yin], Chen, H.[He], Liu, X.[Xiang], Zhang, T.[Tong], Li, L.L.[Lian-Lin], Dong, S.[Shan], Sang, Q.B.[Qian-Bo],
FSoD-Net: Full-Scale Object Detection From Optical Remote Sensing Imagery,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI 2112
Object detection, Remote sensing, Optical imaging, Feature extraction, Detectors, Optical detectors, optical remote sensing BibRef

Bilodeau, M.F.[Mathieu F.], Esau, T.J.[Travis J.], Farooque, A.A.[Aitazaz A.], Zaman, Q.U.[Qamar U.], Heung, B.[Brandon],
Estimation of Agricultural Dykelands Cultivated in Nova Scotia Using Land Property Boundaries and Crop Inventory,
IJGI(10), No. 12, 2021, pp. xx-yy.
DOI Link 2112
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Brauchler, M.[Melanie], Stoffels, J.[Johannes], Nink, S.[Sascha],
Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2,
RS(14), No. 3, 2022, pp. xx-yy.
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Vizzari, M.[Marco],
PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
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Gonçalves, V.P.[Vinicius Paiva], Ribeiro, E.A.W.[Eduardo Augusto Werneck], Imai, N.N.[Nilton Nobuhiro],
Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Muhammad, U.[Usman], Hoque, M.Z.[Md Ziaul], Wang, W.Q.[Wei-Qiang], Oussalah, M.[Mourad],
Patch-Based Discriminative Learning for Remote Sensing Scene Classification,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
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Jiao, S.H.[Shu-Hui], Shen, Z.F.[Zhan-Feng], Kou, W.Q.[Wen-Qi], Wang, H.Y.[Hao-Yu], Li, J.L.[Jun-Li], Jiao, Z.H.[Zhi-Hao], Lei, Y.T.[Ya-Ting],
Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Liu, L.R.[Li-Rong], Tang, X.M.[Xin-Ming], Gan, Y.H.[Yu-Hang], You, S.C.[Shu-Cheng], Luo, Z.Y.[Zheng-Yu], Du, L.[Lei], He, Y.[Yun],
Research on Optimization of Processing Parcels of New Bare Land Based on Remote Sensing Image Change Detection,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Gu, L.Y.[Ling-Yun], Fang, Q.Y.[Qing-Yun], Wang, Z.[Zhaokui], Popov, E.[Eugene], Dong, G.[Ge],
Learning Lightweight and Superior Detectors with Feature Distillation for Onboard Remote Sensing Object Detection,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Luo, W.[Weiran], Zhang, C.[Chengcai], Li, Y.[Ying], Yan, Y.N.[Ya-Ning],
MLGNet: Multi-Task Learning Network with Attention-Guided Mechanism for Segmenting Agricultural Fields,
RS(15), No. 16, 2023, pp. 3934.
DOI Link 2309
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Thabeng, O.L.[Olaotse Lokwalo], Adam, E.[Elhadi], Merlo, S.[Stefania],
Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi-Limpopo Confluence Area,
RS(15), No. 23, 2023, pp. 5491.
DOI Link 2312
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Islam, T.[Touhid], Islam, R.[Rashedul], Uddin, P.[Palash], Ulhaq, A.[Anwaar],
Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery,
RS(16), No. 5, 2024, pp. 807.
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Lee, C.H.[Chun-Han], Chen, K.Y.[Kuang-Yu], Liu, L.Y.D.[Li-Yu Daisy],
Effect of Texture Feature Distribution on Agriculture Field Type Classification with Multitemporal UAV RGB Images,
RS(16), No. 7, 2024, pp. 1221.
DOI Link 2404
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He, C.K.[Chao-Kang], Wang, Q.J.[Qin-Jun], Yang, J.Y.[Jing-Yi], Xu, W.T.[Wen-Tao], Yuan, B.Q.[Bo-Qi],
BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index,
RS(16), No. 9, 2024, pp. 1534.
DOI Link 2405
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Yang, R.[Rui], Qi, Y.[Yuan], Zhang, H.[Hui], Wang, H.W.[Hong-Wei], Zhang, J.L.[Jin-Long], Ma, X.F.[Xiao-Fang], Zhang, J.[Juan], Ma, C.[Chao],
A Study on the Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province,
RS(16), No. 13, 2024, pp. 2479.
DOI Link 2407
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Hassanpour, R.[Reza], Majnooni-Heris, A.[Abolfazl], Fard, A.F.[Ahmad Fakheri], Verrelst, J.[Jochem],
Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series,
RS(16), No. 13, 2024, pp. 2284.
DOI Link 2407
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Xu, W.M.[Wei-Ming], Wang, J.[Juan], Wang, C.J.[Cheng-Jun], Li, Z.W.[Zi-Wei], Zhang, J.C.[Jian-Chang], Su, H.[Hua], Wu, S.[Sheng],
Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images,
RS(16), No. 14, 2024, pp. 2637.
DOI Link 2408
BibRef

Liu, G.H.[Guo-Hong], Liu, C.[Cong], Wu, X.Y.[Xian-Yun], Li, Y.S.[Yun-Song], Zhang, X.[Xiao], Xu, J.J.[Jun-Jie],
Optimization of Remote-Sensing Image-Segmentation Decoder Based on Multi-Dilation and Large-Kernel Convolution,
RS(16), No. 15, 2024, pp. 2851.
DOI Link 2408
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Du, J.N.[Jing-Nan], Xu, S.[Sucheng], Li, J.S.[Jin-Shan], Duan, J.[Jiakun], Xiao, W.[Wu],
A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing,
RS(16), No. 16, 2024, pp. 2981.
DOI Link 2408
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Shi, W.Y., Sui, H.G., Zhang, C.L., Zhou, N., Zhou, M.T., Wang, J.X., Du, Z.T.,
Efficient metric-resolution land cover mapping using open-access low resolution annotations with prototype learning and modified Segment Anything model,
PandRS(224), 2025, pp. 19-41.
Elsevier DOI 2505
Metric-resolution, Land cover mapping, Prototype learning, Segment Anything Model, Label refinement BibRef

Seo, M.[Minseok], Lee, H.[Hakjin], Jeon, Y.J.[Yong-Jin], Seo, J.[Junghoon],
Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery,
WACV23(6363-6372)
IEEE DOI 2302
Training, Deep learning, Visualization, Costs, Biological system modeling, Semantics, Supervised learning, Environmental monitoring/climate change/ecology BibRef

Fu, W.[Wei], Yang, L.S.[Li-Shuang],
Remote Sensing Image Scene Classification via Multi-Level Representation Learning,
ICPR22(2942-2948)
IEEE DOI 2212
How to add global info. CNN for pixel, Attention Network for patches. Deep learning, Representation learning, Measurement, Image analysis, Fuses, Semantics, Feature extraction BibRef

Ozturk, M.Y., Colkesen, I.,
Evaluation of Effectiveness of Patch Based Image Classification Technique Using High Resolution Worldview-2 Image,
SmartCityApp21(417-423).
DOI Link 2201
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McLaughlin, C.[Connor], Woodley, A.[Alan], Geva, S.[Shlomo], Chappell, T.[Timothy], Kelly, W.[Wayne], Boles, W.[Wageeh], de Vine, L.[Lance], Hutson, H.[Holly],
Object Based Remote Sensing Using Sentinel Data,
DICTA20(1-7)
IEEE DOI 2201
Earth, Image analysis, Satellites, Digital images, Vegetation mapping, Australia, Remote sensing, remote sensing, object-based image analysis BibRef

Innani, S.[Shubham], Dutande, P.[Prasad], Baheti, B.[Bhakti], Talbar, S.[Sanjay], Baid, U.[Ujjwal],
Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentation in Aerial Agricultural Images,
AgriVision21(2954-2962)
IEEE DOI 2109
Deep learning, Training, Image segmentation, Semantics, Data integration, Computer architecture BibRef

Chen, T., Wang, H., Liu, H., Du, J., Wu, P.,
An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network,
CVIDL20(100-105)
IEEE DOI 2102
convolutional neural nets, geophysical image processing, image classification, image segmentation, Features fusion BibRef

Gujrathi, A., Yang, C., Rottensteiner, F., Buddhiraju, K.M., Heipke, C.,
Improving the Classification of Land Use Objects Using Dense Connectivity of Convolutional Neural Networks,
ISPRS20(B2:667-673).
DOI Link 2012
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Zheng, Z.[Zhe], Lei, L.[Lin], Sun, H.[Hao], Kuang, G.Y.[Gang-Yao],
A Review of Remote Sensing Image Object Detection Algorithms Based on Deep Learning,
ICIVC20(34-43)
IEEE DOI 2009
Object detection, Remote sensing, Machine learning, Feature extraction, Convolutional neural networks, Convolutional neural networks BibRef

Li, C., Yang, T., Zhu, S., Chen, C., Guan, S.,
Density Map Guided Object Detection in Aerial Images,
EarthVision20(737-746)
IEEE DOI 2008
Agriculture, Object detection, Detectors, Proposals, Task analysis, Image resolution BibRef

Sheng, H., Chen, X., Su, J., Rajagopal, R., Ng, A.,
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture,
AgriVision20(267-276)
IEEE DOI 2008
Vegetation mapping, Indexes, Remote sensing, Data integration, Agriculture, Soil, Satellites BibRef

Aung, H.L., Uzkent, B., Burke, M., Lobell, D., Ermon, S.,
Farm Parcel Delineation Using Spatio-temporal Convolutional Networks,
AgriVision20(340-349)
IEEE DOI 2008
Satellites, Task analysis, Image segmentation, Meteorology, Machine learning, Computer science BibRef

Michaelsen, E., Meidow, J.,
Design of Orientation Assessment Functions for Gestalt-grouping Utilizing Labeled Sample-data,
PIA19(169-173).
DOI Link 1912
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Bosch, M., Christie, G., Gifford, C.,
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery,
WACV19(648-656)
IEEE DOI 1904
image segmentation, learning (artificial intelligence), neural nets, object detection, semantic networks, Training data BibRef

Zhang, Q.Q.[Qian-Qian], Liu, Y.Z.[Ya-Zhou], Sun, Q.S.[Quan-Sen],
Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing,
ICPR21(9507-9513)
IEEE DOI 2105
Learning systems, Hash functions, Image processing, Memory management, object classification BibRef

Kang, T., Liu, Y.Z.[Ya-Zhou], Sun, Q.S.[Quan-Sen],
Object Classification of Remote Sensing Images Based on Partial Randomness Supervised Discrete Hashing,
ICPR18(1935-1940)
IEEE DOI 1812
Remote sensing, Binary codes, Linear programming, Training data, Training, Optimization, Semantics, object classification, partial randomness BibRef

Tian, C., Li, C., Shi, J.,
Dense Fusion Classmate Network for Land Cover Classification,
DeepGlobe18(262-2624)
IEEE DOI 1812
Roads, Image segmentation, Semantics, Task analysis, Training, Convolution, Satellites BibRef

Samy, M., Amer, K., Eissa, K., Shaker, M., ElHelw, M.,
NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation,
DeepGlobe18(267-2674)
IEEE DOI 1812
Image segmentation, Semantics, Roads, Convolution BibRef

Seferbekov, S., Iglovikov, V., Buslaev, A., Shvets, A.,
Feature Pyramid Network for Multi-class Land Segmentation,
DeepGlobe18(272-2723)
IEEE DOI 1812
Image segmentation, Satellites, Training, Feature extraction, Convolution, Semantics BibRef

Pascual, G., Segui, S., Vitria, J.,
Uncertainty Gated Network for Land Cover Segmentation,
DeepGlobe18(276-2763)
IEEE DOI 1812
Uncertainty, Image segmentation, Logic gates, Semantics, Measurement uncertainty, Image resolution BibRef

Kuo, T., Tseng, K., Yan, J., Liu, Y., Wang, Y.F.,
Deep Aggregation Net for Land Cover Classification,
DeepGlobe18(247-2474)
IEEE DOI 1812
Semantics, Image segmentation, Feature extraction, Tuning, Satellites, Decoding, Convolution BibRef

Ghosh, A., Ehrlich, M., Shah, S., Davis, L., Chellappa, R.,
Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery,
DeepGlobe18(252-2524)
IEEE DOI 1812
Semantics, Image segmentation, Remote sensing, Convolution, Training, Satellites, Computer architecture BibRef

Zaouali, M.[Mariem], Bouzidi, S.[Sonia], Zagrouba, E.[Ezzeddine],
Shearlet-Based Region Map Guidance for Improving Hyperspectral Image Classification,
ACIVS17(191-202).
Springer DOI 1712
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Blaschke, T., Lang, S., Tiede, D., Papadakis, M., Györi, A.,
Object-based Image Analysis Beyond Remote Sensing: The Human Perspective,
ISPRS16(B7: 879-882).
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Kupidura, P., Osinska-Skotak, K., Pluto-Kossakowska, J.,
Automatic Approach to VHR Satellite Image Classification,
ISPRS16(B7: 277-282).
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Spectral and texture measures. BibRef

Cui, W.H.[Wei-Hong], Wang, G.F.[Guo-Feng], Feng, C.Y.[Chen-Yi], Zheng, Y.W.[Yi-Wei], Li, J.[Jonathan], Zhang, Y.[Yi],
SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images,
ISPRS16(B7: 195-203).
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Hingee, K.L.,
Statistics For Patch Observations,
ISPRS16(B6: 235-242).
DOI Link 1610
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Kamiya, K., Fuse, T., Takahashi, M.,
Applicability Evaluation Of Object Detection Method To Satellite And Aerial Imageries,
ISPRS16(B7: 229-234).
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Kavzoglu, T., Erdemir, M.Y.[M. Yildiz], Tonbul, H.,
A Region-based Multi-scale Approach For Object-based Image Analysis,
ISPRS16(B7: 241-247).
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Li, C.K., Fang, W., Dong, X.J.,
Research on the Classification of High Resolution Image Based on Object-oriented and Class Rule,
IWIDF15(75-80).
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Wang, G., Liu, J., He, G.,
Object-Based Land Cover Classification for ALOS Image Combining TM Spectral,
SSG13(263-266).
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Djerriri, K., Malki, M.,
Data Mining for Knowledge Discovery from Object-Based Segmentation of VHR Remotely Sensed Imagery,
Hannover13(87-92).
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Region based analysis BibRef

Firat, O.[Orhan], Can, G.[Gulcan], Vural, F.T.Y.[Fatos Tunay Yarman],
Representation Learning for Contextual Object and Region Detection in Remote Sensing,
ICPR14(3708-3713)
IEEE DOI 1412
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And: A2, A1, Only:
Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection,
SSSPR12(216-224).
Springer DOI 1211
Airplanes BibRef

Kanjir, U., Veljanovski, T., Marsetic, A., Oštir, K.,
Application of Object Based Approach to Heterogeneous Land Cover/Use,
GEOBIA10(xx-yy).
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Lizarazo, I.[Ivan],
Fuzzy Image Regions for Quantitative Land Cover Analysis,
GEOBIA10(xx-yy).
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Malinverni, E.S., Tassetti, A.N., Bernardini, A.,
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Ons, G.[Ghariani], Tebourbi, R.[Riadh],
Object oriented hierarchical classification of high resolution remote sensing images,
ICIP09(1681-1684).
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Yu, H.Y.[Hai-Yang], Gan, F.P.[Fu-Ping],
Object recognition of high resolution remote sensing image based on PSWT,
IASP09(52-56).
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Argany, M., Amini, J.,
Artificial neural networks for improvement of classification accuracy in Landsat ETM+ images,
OBIA06(xx-yy).
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Jia, Z., Liu, X.,
Study and application of a multi-resolution hierarchy remote sensing image classification,
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Chmiel, J.,
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Müterthies, M., Buck, O.,
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Lück, W.,
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Lübker, T., Schaab, G.,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Agricultural Field Extraction .