Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images
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Bai, Xiao
Wang, Shuai
Zhou, Jun
Ren, Peng
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Abstract
Object detection plays an active role in remote sensing applications. Recently, deep convolutional neural network models have been applied to automatically extract features, generate region proposals, and predict corresponding object class. However, these models face new challenges in VHR remote sensing images due to the orientation and scale variations and the cluttered background. In this letter, we propose an end-to-end multiscale visual attention networks (MS-VANs) method. We use skip-connected encoder-decoder model to extract multiscale features from a full-size image. For feature maps in each scale, we learn a visual attention network, which is followed by a classification branch and a regression branch, so as to highlight the features from object region and suppress the cluttered background. We train the MS-VANs model by a hybrid loss function which is a weighted sum of attention loss, classification loss, and regression loss. Experiments on a combined data set consisting of Dataset for Object Detection in Aerial Images and NWPU VHR-10 show that the proposed method outperforms several state-of-the-art approaches.
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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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16
Issue
2
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Subject
Artificial intelligence
Electronics, sensors and digital hardware
Geomatic engineering