22.1.5.4 Object Based Land Cover, Region Based Land Cover, Land Use Analysis

Chapter Contents (Back)
Object-Based. Region-Based.

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.[Guonian],
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

Lv, Z.Y.[Zhi-Yong], Liu, T.[Tongfei], Zhang, P.[Penglin], Benediktsson, J.A.[Jón Atli], Chen, Y.[Yixiang],
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

Lu, L.Z.[Li-Zhen], Huang, Y.L.[Yan-Lin], Di, L.P.[Li-Ping], Hang, D.[Danwei],
A New Spatial Attraction Model for Improving Subpixel Land Cover Classification,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
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.[Pengfeng], 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.[Xuguo], 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.[Gongjian], Liu, J.[Jia], Deng, Z.P.[Zhi-Peng], Fan, Y.[Yaxiang],
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

Aguilar, R.[Rosa], Zurita-Milla, R.[Raul], Izquierdo-Verdiguier, E.[Emma], de By, R.A.[Rolf A.],
A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
The regions are small. Worldview-2 images. 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

Sitokonstantinou, V.[Vasileios], Papoutsis, I.[Ioannis], Kontoes, C.[Charalampos], Arnal, A.L.[Alberto Lafarga], Andrés, A.P.A.[Ana Pilar Armesto], Zurbano, J.A.G.[José Angel Garraza],
Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Li, P.[Peng], Ren, P.[Peng], Zhang, X.[Xiaoyu], 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

Ventura, D.[Daniele], Bonifazi, A.[Andrea], Gravina, M.F.[Maria Flavia], Belluscio, A.[Andrea], Ardizzone, G.[Giandomenico],
Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA),
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
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

Peng, J.J.[Jing-Jing], Fan, W.J.[Wen-Jie], Wang, L.Z.[Li-Zhao], Xu, X.[Xiru], Li, J.[Jvcai], Zhang, B.T.[Bei-Tong], Tian, D.F.[Ding-Fang],
Modeling the Directional Clumping Index of Crop and Forest,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
Depending on the angle, objects (i.e. trees) have very different spatial properties, cover more of the scene. 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

Song, H.[Hunsoo], Kim, Y.[Yonghyun], Kim, Y.[Yongil],
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.[Lingbo], 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.[Guanzhou], Tan, X.L.[Xiao-Liang], Zhang, L.[Lifei], Dai, F.[Fan], Liao, P.[Puyun], Gong, Y.[Yuanfu],
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

Zhang, X.[Xiao], Liu, L.Y.[Liang-Yun], Chen, X.[Xidong], 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

Csillik, O.[Ovidiu], Belgiu, M.[Mariana], Asner, G.P.[Gregory P.], Kelly, M.[Maggi],
Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
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

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


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

Kang, T., Liu, Y., Sun, Q.,
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, Computer architecture, Convolution, Computer vision, Pattern recognition 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, Computer vision 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, Computer vision, 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
BibRef

Blaschke, T., Lang, S., Tiede, D., Papadakis, M., Györi, A.,
Object-based Image Analysis Beyond Remote Sensing: The Human Perspective,
ISPRS16(B7: 879-882).
DOI Link 1610
BibRef

Kupidura, P., Osinska-Skotak, K., Pluto-Kossakowska, J.,
Automatic Approach to VHR Satellite Image Classification,
ISPRS16(B7: 277-282).
DOI Link 1610
Spectral and texture measures. BibRef

Cui, W.H.[Wei-Hong], Wang, G.[Guofeng], Feng, C.[Chenyi], Zheng, Y.[Yiwei], Li, J.[Jonathan], Zhang, Y.[Yi],
SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images,
ISPRS16(B7: 195-203).
DOI Link 1610
BibRef

Hingee, K.L.,
Statistics For Patch Observations,
ISPRS16(B6: 235-242).
DOI Link 1610
BibRef

Kamiya, K., Fuse, T., Takahashi, M.,
Applicability Evaluation Of Object Detection Method To Satellite And Aerial Imageries,
ISPRS16(B7: 229-234).
DOI Link 1610
BibRef

Kavzoglu, T., Erdemir, M.Y.[M. Yildiz], Tonbul, H.,
A Region-based Multi-scale Approach For Object-based Image Analysis,
ISPRS16(B7: 241-247).
DOI Link 1610
BibRef

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).
DOI Link 1508
BibRef

Wang, G., Liu, J., He, G.,
Object-Based Land Cover Classification for ALOS Image Combining TM Spectral,
SSG13(263-266).
DOI Link 1402
BibRef

Djerriri, K., Malki, M.,
Data Mining for Knowledge Discovery from Object-Based Segmentation of VHR Remotely Sensed Imagery,
Hannover13(87-92).
DOI Link 1308
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
BibRef
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).
PDF File. 1007
BibRef

Lizarazo, I.[Ivan],
Fuzzy Image Regions for Quantitative Land Cover Analysis,
GEOBIA10(xx-yy).
WWW Link. 1007
BibRef

Malinverni, E.S., Tassetti, A.N., Bernardini, A.,
Automatic Land Use/Land Cover Classification System with Rules Based Both on Objects Attributes and Landscape Indicators,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Ons, G.[Ghariani], Tebourbi, R.[Riadh],
Object oriented hierarchical classification of high resolution remote sensing images,
ICIP09(1681-1684).
IEEE DOI 0911
BibRef

Yu, H.Y.[Hai-Yang], Gan, F.P.[Fu-Ping],
Object recognition of high resolution remote sensing image based on PSWT,
IASP09(52-56).
IEEE DOI 0904
BibRef

Argany, M., Amini, J.,
Artificial neural networks for improvement of classification accuracy in Landsat ETM+ images,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Jia, Z., Liu, X.,
Study and application of a multi-resolution hierarchy remote sensing image classification,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Corcoran, P., Winstanley, A.,
Using texture to tackle the problem of scale in land-cover classification,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Chmiel, J.,
Example of object based approach in land cover classification of VHR satellite image for agricultural areas,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Delenne, C., Rabatel, G., Agurto, V., Deshayes, M.,
Vine plot detection in aerial images using Fourier analysis,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Tarantino, E., Caprioli, M.,
Multiscale representation of brownfield sites with IKONOS imagery,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Preiner, M., Weinke, E., Lang, S.,
Two structure-related strategies for automatically delineating and classifying habitats in an alpine environment,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Kamagata, N., Hara, K., Mori, M., Akamatsu, Y., Li, Y., Hoshino, Y.,
A new method of vegetation mapping by object-based classification using high resolution satellite data,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Luscier, J.D., Thompson, W.L., Wilson, J.M., Gorham, B.E., Dragut, L.D.,
Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Müterthies, M., Buck, O.,
DeCOVER: Developing a methodology to update land cover data for public authorities in Germany,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Lück, W.,
An object oriented data model for a high end land cover classification product,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Lübker, T., Schaab, G.,
Optimization of Parameter Settings for Multilevel Image Segmentation in GEOBIA,
HighRes09(xx-yy).
PDF File. 0906
GEOBIA: GEOgraphic Object-Based Image Analysis. BibRef

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Irrigation Monitoring, Irrigated Field Detection, Land Use Analysis .


Last update:Oct 1, 2019 at 15:23:24