Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold

Abstract

Semantic segmentation is an important method to implement fine-grained semantically understand for high-resolution remote sensing images by dividing images into pixel groupings which can then be labelled and classified. In the field of computer vision (CV), the methods based on fully convolutional network (FCN) are the hotspot and have achieved state-of-the-art results. Compared with popular datasets in CV such as PASCAL and COCO, class imbalance is a problem for multiclass semantic segmentation in remote sensing datasets. In this paper, an FCN-based model is proposed to implement pixel-wise classifications for remote sensing image in an end-to-end way, and an adaptive threshold algorithm is proposed to adjust the threshold of Jaccard index in each class. Experiments on DSTL dataset show that the proposed method produces accurate classifications in an end-to-end way. Results show that the adaptive threshold algorithm can increase the score of average Jaccard index from 0.614 to 0.636 and achieve better segmentation results.