Fast and accurate land-cover classification on medium-resolution remote-sensing images using segmentation models

Abstract

ABSTRACT Land-cover classification especially global mapping has become a new trend in recent years. Traditional convolutional neural network (CNN) methods for land-cover classification are usually patch based and have problems of high computation cost and low efficiency, which hinder their wide applications when timely and accurately mapping land covers. Fortunately, methods based on the fully convolutional network (FCN) have achieved state-of-the-art performance on the semantic segmentation task, which provides a new possibility for efficient land-cover classification. Many works have been done for land-cover classification but are almost focused on very high-resolution remote-sensing images and few research works are implemented on medium-resolution images. In this paper, six representative state-of-the-art segmentation models including ‘U’-shaped network (U-Net), fully convolutional DenseNet (FC-DenseNet), full-resolution residual network (FRRN), bilateral segmentation network (BiSeNet), DeepLab version 3 plus (DeepLabV3+), and pyramid scene parsing network (PSPNet) are selected to compare their performances on the land-cover classification of Land Remote-Sensing Satellite System)-5 satellite remote-sensing images. Based on the analysis of their performances, an improved model named atrous spatial pyramid pooling U-Net (ASPP-U-Net) is proposed for classification. Methods including support vector machine, patch-based CNN, and U-Net are also selected for comparison with the proposed model. Furthermore, to overcome the insufficiency of reference data when training deep models, an integration strategy based on two existing global land-cover products finer resolution observation and monitoring of global land cover of 2010 and global land-cover mapping at 30 m resolution is designed to produce reference data. Experimental results show that the encoder–decoder architecture especially U-Net is the most competitive network and is highly recommended for mapping land covers of medium-resolution images. The proposed ASPP-U-Net outperforms other compared methods not only in the classification accuracy but also in the inference time efficiency. In addition, it is advisable to use existing global land-cover products to produce reference data for segmentation models when the labelled datasets are insufficient.