Mask DeepLab: End-to-end image segmentation for change detection in high-resolution remote sensing images
Yanheng Wang,Yanheng Wang,Lianru Gao,Danfeng Hong,Jian-jun Sha,Lian Liu,Bing Zhang,Xianhui Rong,Yonggang Zhang +8 more
TLDR
Wang et al. as discussed by the authors developed a feature-regularized mask DeepLab (FRM-DeepLab) for remote sensing image change detection, which uses a few annotated samples to update model parameters.About:
This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2021-12-15 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Autoencoder & Overfitting.read more
Citations
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Journal ArticleDOI
A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection
TL;DR: This article proposes a CNN-transformer network with multiscale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland CD.
Journal ArticleDOI
SCViT: A Spatial-Channel Feature Preserving Vision Transformer for Remote Sensing Image Scene Classification
TL;DR: In this paper , a spatial-channel feature preserving vision transformer model (SCViT) is proposed, which considers both the detailed geometric information of the high spatial resolution (HSR) imagery and the contribution of the different channels contained in the classification token.
Journal ArticleDOI
A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images
TL;DR: DARNet is a densely attentive refinement network based on the U-shape encoder–decoder architecture with the Siamese network as a feature extractor to improve change detection on bitemporal very-high-resolution remote sensing images.
Journal ArticleDOI
Fine-Scale Urban Informal Settlements Mapping by Fusing Remote Sensing Images and Building Data via a Transformer-Based Multimodal Fusion Network
TL;DR: Fan et al. as mentioned in this paper proposed a UIS semantic segmentation method, namely UisNet, that utilizes a transformer-based block to receive multimodal data, including high-spatial-resolution remote sensing images (parcel- and pixel-level) and building polygon data (object-level).
Journal ArticleDOI
Joint Variation Learning of Fusion and Difference Features for Change Detection in Remote Sensing Images
TL;DR: In this paper , a self-weighted spatial-temporal attention network (SSANet) is proposed for remote sensing image change detection, which consists of a fusion sub-network, a difference subnetwork, and a decoder.
References
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Book ChapterDOI
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
TL;DR: This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.
Journal ArticleDOI
Review Article Digital change detection techniques using remotely-sensed data
TL;DR: An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
Proceedings Article
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TL;DR: DeepLab as mentioned in this paper combines the responses at the final layer with a fully connected CRF to localize segment boundaries at a level of accuracy beyond previous methods, achieving 71.6% IOU accuracy in the test set.
Journal ArticleDOI
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Hao Chen,Zhenwei Shi +1 more
TL;DR: This work proposes a novel Siamese-based spatial–temporal attention neural network, which improves the F1-score of the baseline model from 83.9 to 87.3 with acceptable computational overhead and introduces a CD dataset LEVIR-CD, which is two orders of magnitude larger than other public datasets of this field.
Journal ArticleDOI
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks
TL;DR: This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning that accomplishes the detection of the changed and unchanged areas by designing a deep neural network.
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