Simultaneous Segmentation And Edge Detection For Hyperspectral Image Via A Deep Supervised And Boundary-Constrained Network

Recent research has shown the great potential of convolutional neural networks (CNNs) in hyperspectral image (HSI) classification. Nevertheless, CNN based approaches may lead to over-smoothing effect due to the spatial information loss during the convolution and pooling operations. To address this problem, in this paper, we propose a deep supervised and boundary-constrained network (DSBC-Net) which takes the boundary information into consideration. With the well-designed architecture, DSBC-Net can achieve segmentation and edge detection for HSI simultaneously. Besides, a novel deep supervision strategy is proposed to improve the training of the deep neural network. Experimental results on two benchmark HSI datasets demonstrate that the DSBC-Net can better maintain the boundaries of different objects with higher classification accuracy compared with previous methods.