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

Chapter Contents (Back)
Object-Based. Region-Based. Patch-Based. GEOBIA
See also Agricultural Field Extraction.
See also Remote Sensing Semantic Segmentation.

Yu, Q.[Qian], Gong, P.[Peng], Clinton, N.[Nick], Biging, G.[Greg], Kelly, M.[Maggi], Schirokauer, D.[Dave],
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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,
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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.,
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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],
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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,
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BibRef

Wolf, N.[Nils],
Feature Evaluation for a Transferable Approach of Object-based Land Cover Classification Based on Ikonos and QuickBird Satellite Data,
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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
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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,
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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,
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DOI Link 1312
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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
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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,
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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.
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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.
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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
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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
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Gerçek, D.[Deniz],
A Conceptual Model for Delineating Land Management Units (LMUs) Using Geographical Object-Based Image Analysis,
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Gerçek, D.[Deniz], Zeydanl, U.[Ugur],
Object-Based Classification of Landscape into Land Management Units (LMUs),
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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,
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Zhang, X.L.[Xue-Liang], Xiao, P.[Pengfeng], Feng, X.Z.[Xue-Zhi],
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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.
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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
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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
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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.
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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.
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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.
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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,
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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,
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Li, P.[Peng], Ren, P.[Peng], Zhang, X.Y.[Xiao-Yu], Wang, Q.[Qian], Zhu, X.O.[Xia-Obin], Wang, L.[Lei],
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Elsevier DOI 1807
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Elsevier DOI 1809
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Chmielewski, S.[Szymon], Bochniak, A.[Andrzej], Natapov, A.[Asya], Wezyk, P.[Piotr],
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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
BibRef

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.
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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
BibRef

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.
DOI Link 2104
BibRef

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

Zhang, P.[Peng], Hu, S.[Shougeng], Li, W.D.[Wei-Dong], Zhang, C.R.[Chuan-Rong], Cheng, P.[Peikun],
Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
DOI Link 2202
BibRef

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
BibRef

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
BibRef

Cai, Z.W.[Zhi-Wen], Hu, Q.[Qiong], Zhang, X.Y.[Xin-Yu], Yang, J.Y.[Jing-Ya], Wei, H.D.[Hao-Dong], He, Z.[Zhen], Song, Q.[Qian], Wang, C.[Cong], Yin, G.F.[Gao-Fei], Xu, B.D.[Bao-Dong],
An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, Z.Q.[Zhi-Qi], Lu, W.[Wen], Cao, J.S.[Jin-Shan], Xie, G.Q.[Guang-Qi],
MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

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
BibRef

Xing, H.Q.[Hua-Qiao], Chen, B.Y.[Bing-Yao], Lu, M.[Miao],
A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

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
BibRef

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
BibRef

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
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
BibRef

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
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

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, Pattern recognition, 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, 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 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).
DOI Link 1610
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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
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).
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Lizarazo, I.[Ivan],
Fuzzy Image Regions for Quantitative Land Cover Analysis,
GEOBIA10(xx-yy).
WWW Link. 1007
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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,
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Ons, G.[Ghariani], Tebourbi, R.[Riadh],
Object oriented hierarchical classification of high resolution remote sensing images,
ICIP09(1681-1684).
IEEE DOI 0911
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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).
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
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Jia, Z., Liu, X.,
Study and application of a multi-resolution hierarchy remote sensing image classification,
OBIA06(xx-yy).
PDF File. 0607
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Corcoran, P., Winstanley, A.,
Using texture to tackle the problem of scale in land-cover classification,
OBIA06(xx-yy).
PDF File. 0607
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Chmiel, J.,
Example of object based approach in land cover classification of VHR satellite image for agricultural areas,
OBIA06(xx-yy).
PDF File. 0607
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Delenne, C., Rabatel, G., Agurto, V., Deshayes, M.,
Vine plot detection in aerial images using Fourier analysis,
OBIA06(xx-yy).
PDF File. 0607
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Tarantino, E., Caprioli, M.,
Multiscale representation of brownfield sites with IKONOS imagery,
OBIA06(xx-yy).
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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,
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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).
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Müterthies, M., Buck, O.,
DeCOVER: Developing a methodology to update land cover data for public authorities in Germany,
OBIA06(xx-yy).
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Lück, W.,
An object oriented data model for a high end land cover classification product,
OBIA06(xx-yy).
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Lübker, T., Schaab, G.,
Optimization of Parameter Settings for Multilevel Image Segmentation in GEOBIA,
HighRes09(xx-yy).
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Agricultural Field Extraction .


Last update:Aug 31, 2023 at 09:37:21