1. A spatial-channel progressive fusion ResNet for remote sensing classification.
- Author
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Zhu, Hao, Ma, Mengru, Ma, Wenping, Jiao, Licheng, Hong, Shikuan, Shen, Jianchao, and Hou, Biao
- Subjects
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REMOTE sensing , *DISCRETE wavelet transforms , *MULTISENSOR data fusion , *FEATURE extraction , *CLASSIFICATION , *MULTISPECTRAL imaging - Abstract
In recent years, the panchromatic (PAN) and the multispectral (MS) remote sensing images classification has become a research hotspot. In this paper, we propose a spatial-channel progressive fusion residual network (SCPF-ResNet) for multi-resolution remote sensing classification. Firstly, for the inputs of the proposed network, the interactive data fusion strategy (IDFS) combines generalized-intensity-hue-saturation (GIHS), and discrete wavelet transform (DWT) to interfuse patch pairs of the PAN and the MS images, so as to increase the similarity between them, thus reduce the difference in information between them. Secondly, for the branches of feature extraction, we design an adaptive spatial attention module (ASA-Module) and an adaptive channel attention module (ACA-Module) to strengthen spatial features from both larger-sized with smaller-sized targets and spectral features among channels. Finally, we insert the ASA-Module and ACA-Module into the residual modules to form a triple-branch network and use the common spatial-channel features extracted by the Fusion_Branch to gradually enhance the pure independent features extracted by the PAN_Branch and the MS_Branch, respectively. The experimental results indicate that SCPF-ResNet can achieve competitive performance. • An interactive data fusion strategy is proposed. • Two adaptive attention modules to highlight the advantages of two different data. • A triple-branch progressive fusion residual network for classification is proposed. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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