Deep learning for effective detection of excavated soil related to illegal tunnel activities

This paper presents a new deep learning based approach for soil detection using high resolution multispectral satellite images with a resolution of 0.31 m. In particular, a deep convolutional neural network (CNN) is proposed for soil detection to identify potential tunnel digging activities. Spatial and spectral information in the multispectral image cube has been incorporated into the CNN. We also propose a novel method to handle imbalance learning in the context of deep CNN model training. Experimental results on Worldview-2 (WV-2) multispectral satellite images captured at the border between USA and Mexico showed that the proposed CNN model can effectively detect soil in the remote sensed images, and the proposed imbalance learning technique improved the detection performance significantly.