Application of multi-scale and lightweight CNN in SAR image-based surface feature classification

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Abstract

Targeting the subtropical climate characteristics of the Guangdong-Hong Kong-Macao Greater Bay Area, this study acquired the images of the experimental area from the TerraSAR-X Radar remote sensing satellite. Given the varying scale of the surface feature targets in the Radar satellite observation scenes, this study proposed an ENet convolution spatial pyramid pooling module (ENet-CSPP) model for surface feature classification. Since ordinary convolution can more effectively maintain domain information than atrous convolution, this study proposed a multi-scale feature fusion module based on convolution spatial pyramid pooling. Since there were a few training samples in the SAR remote sensing image dataset, this study combined the multi-scale feature fusion module with the lightweight convolutional neural network. The encoder of the ENet-CSPP network consisted of an improved ENet network and the convolution spatial pyramid pooling module. The decoder output surface feature classification images after the fusion of deep and shallow features. The quantitative comparison experiments were conducted on the GDUT-Nansha dataset. The ENet-CSPP model outperformed other models in three performance indices, namely pixel accuracy, average pixel accuracy, and mean intersection over union. This result indicates that the multi-scale lightweight model effectively improved the accuracy of surface feature classification.

Keywords synthetic aperture Radar(SAR) surface feature classification convolutional neural network lightweight network

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Issue Date: 20 March 2023

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	Sheng SUN
	Zhimin MENG
	Zhongwen HU
	Xu YU

Cite this article:

Sheng SUN,Zhimin MENG,Zhongwen HU, et al. Application of multi-scale and lightweight CNN in SAR image-based surface feature classification[J]. Remote Sensing for Natural Resources, 2023, 35(1): 27-34.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021421 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/27

Tab.1 Comparison of ENet and ENet-CSPP structures

Fig.1 Overall architecture

Fig.2 Partial training set data

Fig.3 Testing set area

Tab.2 Effect of cross entropy loss function category weight on the model(%)

Tab.3 Effect of shallow and deep feature channel numbers on the model(%)

Fig.4 Classification images of the diffeent models on the test image

Tab.4 Comparison of different models on GDUT-Nansha data set(%)

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