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Fully Convolutional Network for Target Classification of Ice Sheet Radar Image

Global warming has exacerbated the melting of glaciers in Greenland and Antarctica, which has considerable influence on sea level rise. The classification of targets in the radar images acquired by airborne radar is helpful to further analyze the evolution of ice sheet subsurface targets. In recent years, deep learning techniques for target detection and classification greatly improved the performance of traditional techniques based on hand-crafted feature engineering. Therefore, this paper proposes a fully convolutional network for the automatic target classification of ice sheet radar image, which is mainly composed of feature extraction module (FEM) and densely dilated convolution module (DDCM). The FEM combines standard convolution and depthwise separable convolution together, which can reduce the parameters and maintain the classification accuracy as much as possible. The DDCM is composed of four dilated convolutions with different dilation rates by dense connection, which is used to capture larger receptive field and obtain high-level semantic features. The proposed network is validated on ice sheet radar image provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011. Experimental results show that the proposed network achieves higher accuracy than the classic backbone networks, and it is about 8 times faster than the latest ice sheet image target classification network to infer each image.