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Deep multi-level fusion network for multi-source image pixel-wise classification
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DOI：10.1016/j.knosys.2021.106921.png)
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Abstract
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For multi-source image pixel-wise classification, each image information is different and complemen-tary in the same area or scene. However, how to integrate them for decision-making is a difficult problem. In this paper, we focus on the characteristics of multi-source image and propose a novel pixel-wise classification method, named deep multi-level fusion network. The proposed method is to classify multi-sensor data including very high-resolution (VHR) RGB imagery, hyperspectral imagery (HSI) and multispectral light detection and ranging (MS-LiDAR) point cloud data. First, a deep spectral-spatial attention network is proposed to process HSI and MS-LiDAR images and get a learned classification map, which is based on feature level fusion. Next, a down-superpixel segmentation algorithm is proposed to get a segmentation result for VHR RGB imagery. Finally, the feature level fusion results are refinement by the down-superpixel segmentation results on the decision level, and get the final result. Extensive experiments and analyses on the data set grss_dfc_2018 demonstrate that the proposed multi-level fusion network can achieve a better result in the multi-source image pixel-wise classification. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Classification
Multi-level fusion
Attention
Segmentation
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