Land cover classification based on the PSPNet and superpixel segmentation methods with high spatial resolution multispectral remote sensing imagery

Abstract

Classifying land cover using high-resolution remote-sensing images is challenging. The emergence of deep learning provides improved possibilities, but owing to the limitations of network structures, traditional convolutional neural network methods lose essential information about boundaries and small ground objects. We propose a superpixel-optimized convolutional neural network (SOCNN) framework to overcome this weakness. The SOCNN includes three modules: a semantic segmentation module, a superpixel optimization module, and a fusion module. The performance of the first module was evaluated using several common networks. PSPNet outperformed other networks, obtaining a pixel accuracy of 83.25%, a Kappa coefficient of 0.7862, and a mean intersection over union of 64.19%. Weighted loss was introduced to alleviate the effect of category imbalance, and the class pixel accuracy of category 11 improved by 19.77% with a weight of 20. The subpixel model was evaluated, and the pixel accuracy reached 83.43% with the superpixel-FCN method. Our superpixel optimized module improved the pixel accuracy of the object boundary by 1.37% when the fusion factor was 0.65. These results show that the SOCNN method is effective for recovering boundary information.