Spatial-Spectral Attention Network Guided With Change Magnitude Image for Land Cover Change Detection Using Remote Sensing Images

Land cover change detection (LCCD) using remote sensing images (RSIs) plays an important role in natural disaster evaluation, forest deformation monitoring, and wildfire destruction detection. However, bitemporal images are usually acquired at different atmospheric conditions, such as sun height and...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 12
Hlavní autoři: Lv, Zhiyong, Wang, Fengjun, Cui, Guoqing, Benediktsson, Jon Atli, Lei, Tao, Sun, Weiwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Atmospheric conditions
Change detection
Convolution
Convolutional block attention module (CBAM)
Deformation
Detection
Earth surface
Feature extraction
guide change magnitude image (CMI)
Image acquisition
Land cover
land cover change detection (LCCD)
Mathematical models
Moisture effects
multiscale dilation convolution module (MDCM)
Natural disasters
Neural networks
Remote sensing
remote sensing images (RSIs)
Shape
Smoothing methods
Soil moisture
Temperature
Wildfires
ISSN:0196-2892, 1558-0644
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Shrnutí:Land cover change detection (LCCD) using remote sensing images (RSIs) plays an important role in natural disaster evaluation, forest deformation monitoring, and wildfire destruction detection. However, bitemporal images are usually acquired at different atmospheric conditions, such as sun height and soil moisture, which usually cause pseudo and noise change in the change detection map. Changed areas on the ground also generally have various shapes and sizes, consequently making the utilization of spatial contextual information a challenging task. In this article, we design a novel neural network with a spatial-spectral attention mechanism and multiscale dilation convolution modules. This work is based on the previously demonstrated promising performance of convolutional neural network for LCCD with RSIs and attempts to capture more positive changes and further enhance the detection accuracies. The learning of the proposed neural network is guided with a change magnitude image. The performance and feasibility of the proposed network are validated with four pairs of RSIs that depict real land cover change events on the Earth's surface. Comparison of the performance of the proposed approach with that of five state-of-art methods indicates the superiority of the proposed network in terms of ten quantitative evaluation metrics and visual performance. Such as, the proposed network achieved an improvement of about 0.08%-14.87% in terms of overall accuracy (OA) for Dataset-A.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3197901