注意力引导的三维卷积网络用于遥感场景变化检测

doi:10.3969/j.issn.0255-8297.2021.02.009

Abstract

Abstract: With high tolerance to the great amount of noise and precise depiction of image changes in high resolution remote sensing images (HRRSI), scene-level change detection strategy makes it possible to detect changes in HRRSI. In this paper, we propose an attention guided 3D ConvNet for HRRSI change detection. Firstly, we develop a simplified 3D AlexNet to extract convolutional features. Then we add a semantic attention module (SAM) to further extract the discriminative regions which strongly relate to land-cover changes. Finally, the refined features are fed into a classification layer to organize the whole framework in an end-to-end trainable manner. Scenes in different phases are put into the convolutional neural network (CNN) with the result of change detection. In order to evaluate the performance of scene level change detection methods, we create a public semantic level high resolution remote sensing images change detection benchmark. Experimental results on this dataset are obviously better than other related methods, demonstrate the effectiveness of our method, and show the prospect of scene level remote sensing change detection based on deep learning.

Key words: scene-level change detection, semantic attention module, 3D ConvNet, high resolution remote sensing interpretation, scene-level change detection benchmark

CLC Number:

TP751.1

ZHANG Han, QIN Kun, BI Qi, ZHANG Ye, XU Kai. Attention Guided 3D ConvNet for Aerial Scene Change Detection[J]. Journal of Applied Sciences, 2021, 39(2): 272-280.

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Attention Guided 3D ConvNet for Aerial Scene Change Detection

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