Optical Remote Sensing Water-Land Segmentation Representation Based on Proposed SNS-CNN Network

For water resource analysis applications, due to very high resolution and large observation scope, optical remote sensing images can present more visible object characters. Water-land segmentation from optical remote sensing images is wildly used and becomes a hot research topic. However, since large scale complex background scenes include many interferences, the water-land segmentation from optical remote sensing images becomes a challenge task. Aim to achieve better water area feature description from complex land cover background, we apply a sub-neighbor system convolutional neural network (SNS-CNN) to the water-land segmentation in harbor scene areas. First, on the basis of the U-net structure, an optimized up-sampling process is proposed to enhance water area feature expression. Second, a novel sub-neighbor system constraint of each predicted pixel point is leaded into the loss function to make the model producing water mask more coherent. Furthermore, experiments on our collected variety of optical remote sensing images demonstrate that this paper proposed water-land segmentation method can produce better performance than state of the art methods.