Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area

2017 ◽  
Vol 201 ◽  
pp. 243-255 ◽  
Author(s):  
Xueliang Zhang ◽  
Pengfeng Xiao ◽  
Xuezhi Feng ◽  
Min Yuan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Urban Area ◽  
Remote Sensing Images ◽  
Object Based ◽  
Multi Temporal

A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images

2018 ◽  
Vol 39 (22) ◽  
pp. 7998-8021 ◽  
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu ◽  
Weiming Huang ◽  
Kaimin Sun
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Change Detection ◽  
Visual Saliency ◽  
Remote Sensing Images ◽  
Detection Approach ◽  
Multi Temporal

Unsupervised Object-Based Change Detection via a Weibull Mixture Model-Based Binarization for High-Resolution Remote Sensing Images

2018 ◽  
Vol 15 (1) ◽  
pp. 63-67 ◽  
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Jianwu Fang ◽  
Jianghong Ma ◽  
Xueli Song
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Mixture Model ◽  
Remote Sensing Images ◽  
Model Based ◽  
Object Based

scholarly journals A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

2018 ◽  
Vol 10 (7) ◽  
pp. 1015 ◽  
Author(s):  
Wenqing Feng ◽  
Haigang Sui ◽  
Jihui Tu ◽  
Weiming Huang ◽  
Chuan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Rotation Forest ◽  
Detection Approach ◽  
Multi Temporal ◽  
Uncertainty Analyses ◽  
Coarse To Fine

An object-based graph model for unsupervised change detection in high resolution remote sensing images

2021 ◽  
Vol 42 (16) ◽  
pp. 6212-6230
Author(s):  
Junzheng Wu ◽  
Biao Li ◽  
Yao Qin ◽  
Weiping Ni ◽  
Han Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Graph Model ◽  
Remote Sensing Images ◽  
Object Based

scholarly journals Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 189 ◽  
Author(s):  
Chi Zhang ◽  
Shiqing Wei ◽  
Shunping Ji ◽  
Meng Lu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Urban Land Cover ◽  
Object Based ◽  
Very High ◽  
Change Map

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating.

A scene change detection framework for multi-temporal very high resolution remote sensing images

2016 ◽  
Vol 124 ◽  
pp. 184-197 ◽  
Author(s):  
Chen Wu ◽  
Lefei Zhang ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Scene Change ◽  
Remote Sensing Images ◽  
Scene Change Detection ◽  
Multi Temporal ◽  
Very High
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