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2009
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Elsevier DOI
2009
Deep learning, Convolutional neural network, Land cover, Aerial imagery
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Tran, B.H.[Ba-Huy],
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Elsevier DOI
2011
Object-based image analysis, Curve matching, His-Cov,
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Sun, Y.W.[Ying-Wei],
Shen, Z.F.[Zhan-Feng],
Xu, N.[Nan],
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Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data
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2011
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Priyadarsini, M.J.P.[M. Jasmine Pemeena],
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Puttamadappa, C.,
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Cascade Object Detection and Remote Sensing Object Detection Method
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2101
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Cao, Z.Y.[Zhi-Ying],
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C3Net: Cross-Modal Feature Recalibrated, Cross-Scale Semantic
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2102
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Zhang, W.[Wei],
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2102
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Qu, L.[Le'an],
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Accuracy Improvements to Pixel-Based and Object-Based LULC
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2102
BibRef
Li, X.R.[Xing-Rong],
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Zhang, Q.[Qing],
Identification of Abandoned Jujube Fields Using Multi-Temporal
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2103
BibRef
Gilcher, M.[Mario],
Udelhoven, T.[Thomas],
Field Geometry and the Spatial and Temporal Generalization of Crop
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DOI Link
2103
BibRef
Taravat, A.[Alireza],
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Bonifacio, R.[Rogerio],
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Advanced Fully Convolutional Networks for Agricultural Field Boundary
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RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Luo, C.[Chong],
Qi, B.S.[Bei-Song],
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Shao, Y.Q.[Yi-Qun],
Using Time Series Sentinel-1 Images for Object-Oriented Crop
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RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Xue, Y.[Yongan],
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A Watershed-Segmentation-Based Improved Algorithm for Extracting
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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
Waldner, F.[François],
Diakogiannis, F.I.[Foivos I.],
Batchelor, K.[Kathryn],
Ciccotosto-Camp, M.[Michael],
Cooper-Williams, E.[Elizabeth],
Herrmann, C.[Chris],
Mata, G.[Gonzalo],
Toovey, A.[Andrew],
Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries
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RS(13), No. 11, 2021, pp. xx-yy.
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2106
BibRef
Yang, C.[Chun],
Rottensteiner, F.[Franz],
Heipke, C.[Christian],
A hierarchical deep learning framework for the consistent
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PandRS(177), 2021, pp. 38-56.
Elsevier DOI
2106
Hierarchical consistent land use classification, CNN,
Geospatial database, Aerial imagery
BibRef
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Hu, S.[Shougeng],
Li, W.D.[Wei-Dong],
Zhang, C.R.[Chuan-Rong],
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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
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RS(13), No. 12, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Wei, Z.H.[Zhong-Hui],
Gu, X.H.[Xiao-He],
Sun, Q.[Qian],
Hu, X.Q.[Xue-Qian],
Gao, Y.B.[Yun-Bing],
Analysis of the Spatial and Temporal Pattern of Changes in Abandoned
Farmland Based on Long Time Series of Remote Sensing Data,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Dong, R.[Ruchan],
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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.
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2109
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Xu, Z.Y.[Zhi-Yong],
Zhang, W.C.[Wei-Cun],
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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
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RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
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Torres, R.N.[Rocio Nahime],
Fraternali, P.[Piero],
Learning to Identify Illegal Landfills through Scene Classification
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RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
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
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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
BibRef
Brauchler, M.[Melanie],
Stoffels, J.[Johannes],
Nink, S.[Sascha],
Extension of an Open GEOBIA Framework for Spatially Explicit Forest
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RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Wen, C.[Caiyun],
Lu, M.[Miao],
Bi, Y.[Ying],
Zhang, S.N.[Sheng-Nan],
Xue, B.[Bing],
Zhang, M.J.[Meng-Jie],
Zhou, Q.[Qingbo],
Wu, W.B.[Wen-Bin],
An Object-Based Genetic Programming Approach for Cropland Field
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RS(14), No. 5, 2022, pp. xx-yy.
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2203
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Li, T.[Ting],
Johansen, K.[Kasper],
McCabe, M.F.[Matthew F.],
A machine learning approach for identifying and delineating
agricultural fields and their multi-temporal dynamics using three
decades of Landsat data,
PandRS(186), 2022, pp. 83-101.
Elsevier DOI
2203
Center-pivot field, Delineation, DBSCAN,
Convolution neural networks, Spectral clustering, Random forest
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Lu, R.[Rui],
Wang, N.[Nan],
Zhang, Y.[Yanbin],
Lin, Y.[Yeneng],
Wu, W.Q.[Wen-Qiang],
Shi, Z.[Zhou],
Extraction of Agricultural Fields via DASFNet with Dual Attention
Mechanism and Multi-scale Feature Fusion in South Xinjiang, China,
RS(14), No. 9, 2022, pp. xx-yy.
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2205
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.
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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
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2206
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McLaughlin, C.[Connor],
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Geva, S.[Shlomo],
Chappell, T.[Timothy],
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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
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Meyer, L.,
Lemarchand, F.,
Sidiropoulos, P.,
A Deep Learning Architecture for Batch-mode Fully Automated Field
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2012
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Gujrathi, A.,
Yang, C.,
Rottensteiner, F.,
Buddhiraju, K.M.,
Heipke, C.,
Improving the Classification of Land Use Objects Using Dense
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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
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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
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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
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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
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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
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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, Pattern recognition
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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
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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
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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
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Ehrlich, M.,
Shah, S.,
Davis, L.,
Chellappa, R.,
Stacked U-Nets for Ground Material Segmentation in Remote Sensing
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DeepGlobe18(252-2524)
IEEE DOI
1812
Semantics, Image segmentation, Remote sensing, Convolution, Training,
Satellites, Computer architecture
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Zagrouba, E.[Ezzeddine],
Shearlet-Based Region Map Guidance for Improving Hyperspectral Image
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Springer DOI
1712
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Blaschke, T.,
Lang, S.,
Tiede, D.,
Papadakis, M.,
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Object-based Image Analysis Beyond Remote Sensing:
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ISPRS16(B7: 879-882).
DOI Link
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Osinska-Skotak, K.,
Pluto-Kossakowska, J.,
Automatic Approach to VHR Satellite Image Classification,
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1610
Spectral and texture measures.
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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
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ISPRS16(B7: 195-203).
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Hingee, K.L.,
Statistics For Patch Observations,
ISPRS16(B6: 235-242).
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Kamiya, K.,
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Applicability Evaluation Of Object Detection Method To Satellite And
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Erdemir, M.Y.[M. Yildiz],
Tonbul, H.,
A Region-based Multi-scale Approach For Object-based Image Analysis,
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Research on the Classification of High Resolution Image Based on
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Object-Based Land Cover Classification for ALOS Image Combining TM
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SSG13(263-266).
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Data Mining for Knowledge Discovery from Object-Based Segmentation of
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Region based analysis
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Vural, F.T.Y.[Fatos Tunay Yarman],
Representation Learning for Contextual Object and Region Detection in
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ICPR14(3708-3713)
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1412
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And: A2, A1, Only:
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1211
Airplanes
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Application of Object Based Approach to Heterogeneous Land Cover/Use,
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Artificial neural networks for improvement of classification accuracy
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Jia, Z.,
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Using texture to tackle the problem of scale in land-cover
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Chmiel, J.,
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Delenne, C.,
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Tarantino, E.,
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Luscier, J.D.,
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Müterthies, M.,
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Lück, W.,
An object oriented data model for a high end land cover classification
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Subpixel Target, Subpixel Land Use, Tiny Objects .