Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan-Vese Model
暂无分享,去创建一个
Wenzhong Shi | Xiaokang Zhang | Feifei Peng | Zhiyong Lv | W. Shi | Xiaokang Zhang | Z. Lv | F. Peng
[1] Wenzhong Shi,et al. Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity , 2019, Remote. Sens..
[2] Jon Atli Benediktsson,et al. Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[3] Xiaodong Liu,et al. Multiscale Superpixel Segmentation With Deep Features for Change Detection , 2019, IEEE Access.
[4] Wei Liu,et al. Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks , 2019, ISPRS Int. J. Geo Inf..
[5] Shuying Li,et al. Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks , 2019, IEEE Geoscience and Remote Sensing Letters.
[6] Francesca Bovolo,et al. Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[7] Qian Du,et al. GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[8] Xiao Xiang Zhu,et al. Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[9] Andreas Dengel,et al. Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks , 2017, 2019 IEEE International Conference on Image Processing (ICIP).
[10] Jon Atli Benediktsson,et al. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images , 2018, Remote. Sens..
[11] Xin Pan,et al. An object-based convolutional neural network (OCNN) for urban land use classification , 2018, Remote Sensing of Environment.
[12] Shuying Li,et al. Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images , 2018, Remote. Sens..
[13] Yun Zhang,et al. Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery , 2018, Remote. Sens..
[14] Haiyan Gu,et al. An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery , 2018, Remote. Sens..
[15] Jon Atli Benediktsson,et al. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images , 2018, Remote. Sens..
[16] Wenzhong Shi,et al. Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[17] Qian Du,et al. Multisource Remote Sensing Data Classification Based on Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[18] Asoke K. Nandi,et al. Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering , 2018, IEEE Transactions on Fuzzy Systems.
[19] Yongjun Zhang,et al. Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis , 2018, IEEE Geoscience and Remote Sensing Letters.
[20] Menglong Yan,et al. Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images , 2017, IEEE Geoscience and Remote Sensing Letters.
[21] Mohammed Bennamoun,et al. Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[22] Wenzhong Shi,et al. Level set evolution with local uncertainty constraints for unsupervised change detection , 2017 .
[23] Hao Wu,et al. Convolutional Recurrent Neural Networks forHyperspectral Data Classification , 2017, Remote. Sens..
[24] Qing Wang,et al. Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[25] Maoguo Gong,et al. Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[26] Hannes Taubenböck,et al. Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[27] W. Shi,et al. Level set incorporated with an improved MRF model for unsupervised change detection for satellite images , 2017 .
[28] Wenzhong Shi,et al. Object-oriented change detection method based on adaptive multi-method combination for remote-sensing images , 2016 .
[29] Thomas Blaschke,et al. Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods , 2016, Remote. Sens..
[30] Jia Liu,et al. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images , 2016 .
[31] Wenzhong Shi,et al. Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm , 2016, Remote. Sens..
[32] Wenzhong Shi,et al. Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method , 2016 .
[33] Wenzhong Shi,et al. Validation of Land Cover Products Using Reliability Evaluation Methods , 2015, Remote. Sens..
[34] Suming Jin,et al. Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .
[35] Nicholas J. Tate,et al. A critical synthesis of remotely sensed optical image change detection techniques , 2015 .
[36] Maoguo Gong,et al. A Local Statistical Fuzzy Active Contour Model for Change Detection , 2015, IEEE Geoscience and Remote Sensing Letters.
[37] Biao Wang,et al. Object-Based Change Detection of Very High Resolution Satellite Imagery Using the Cross-Sharpening of Multitemporal Data , 2015, IEEE Geoscience and Remote Sensing Letters.
[38] K. Moffett,et al. Remote Sens , 2015 .
[39] Yupeng Li,et al. Automatic change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine classification , 2014 .
[40] Francesca Bovolo,et al. Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[41] Ashish Ghosh,et al. Semi-supervised change detection using modified self-organizing feature map neural network , 2014, Appl. Soft Comput..
[42] Thomas Blaschke,et al. Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[43] Dongmei Chen,et al. Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .
[44] Francesca Bovolo,et al. Supervised change detection in VHR images using contextual information and support vector machines , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[45] Enhong Chen,et al. Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.
[46] Valentyn Tolpekin,et al. Multitemporal change detection of urban trees using localized region - based active contours in VHR images , 2012 .
[47] Thomas R. Loveland,et al. A review of large area monitoring of land cover change using Landsat data , 2012 .
[48] Zeki Yetgin,et al. Unsupervised Change Detection of Satellite Images Using Local Gradual Descent , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[49] Thomas Blaschke,et al. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems - An Overview , 2011, Remote. Sens..
[50] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[51] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[52] Hanqing Lu,et al. Fast Object-Level Change Detection for VHR Images , 2010, IEEE Geoscience and Remote Sensing Letters.
[53] Turgay Çelik,et al. Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.
[54] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[55] Francesca Bovolo,et al. A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[56] Paul E. Gessler,et al. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments , 2008 .
[57] John R. Jensen,et al. Object‐based change detection using correlation image analysis and image segmentation , 2008 .
[58] Michael E. Hodgson,et al. An automated binary change detection model using a calibration approach , 2007 .
[59] Nazzareno Pierdicca,et al. Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies , 2006 .
[60] John R. Jensen,et al. A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .
[61] S. Carpenter,et al. Global Consequences of Land Use , 2005, Science.
[62] D. Lu,et al. Change detection techniques , 2004 .
[63] Pol Coppin,et al. Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .
[64] Xavier Pons,et al. Post-classification change detection with data from different sensors: Some accuracy considerations , 2003 .
[65] Lorenzo Bruzzone,et al. Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..