Automatic Semantic Segmentation with DeepLab Dilated Learning Network for Change Detection in Remote Sensing Images

2020 ◽  
Vol 51 (3) ◽  
pp. 2355-2377 ◽  
Author(s):  
N. Venugopal
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Learning Network

Unsupervised Multiple Change Detection in Remote Sensing Images via Generative Representation Learning Network

2021 ◽  
pp. 1-5
Author(s):  
Jiao Shi ◽  
Zeping Zhang ◽  
Chunhui Tan ◽  
Xiaodong Liu ◽  
Yu Lei
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Representation Learning ◽  
Remote Sensing Images ◽  
Learning Network

scholarly journals Deep Siamese Networks Based Change Detection with Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3394 ◽  
Author(s):  
Le Yang ◽  
Yiming Chen ◽  
Shiji Song ◽  
Fan Li ◽  
Gao Huang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Change Detection ◽  
Ground Surface ◽  
Semantic Segmentation ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Image Pairs ◽  
Segmentation Task ◽  
High Level

Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

scholarly journals Semantic Segmentation of Remote Sensing Images With Sparse Annotations

2021 ◽  
pp. 1-5
Author(s):  
Yuansheng Hua ◽  
Diego Marcos ◽  
Lichao Mou ◽  
Xiao Xiang Zhu ◽  
Devis Tuia
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

2021 ◽  
pp. 1-5
Author(s):  
Yi Liu ◽  
Chao Pang ◽  
Zongqian Zhan ◽  
Xiaomeng Zhang ◽  
Xue Yang
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Network Model ◽  
Dual Task ◽  
Remote Sensing Images ◽  
Convolutional Network

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images

2021 ◽  
pp. 1-1
Author(s):  
Haeyun Lee ◽  
Kyung su Lee ◽  
Junhee Kim ◽  
Younghwan Na ◽  
Juhum Park ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Urban Land ◽  
Local Similarity ◽  
Land Change ◽  
Remote Sensing Images ◽  
Siamese Network
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