Novel Convolutions for Semantic Segmentation of Remote Sensing Images

Ruijie Xiao, Chuan Zhong, Wankang Zeng, Ming Cheng, Cheng Wang

Abstract: The networks are required to be capable of learning low-level features well when applied to remote sensing image (RSI) semantic segmentation tasks. To capture accurate and abundant low-level semantic information, the early feature extractor layer is crucial to the whole network because all the subsequent features are inferred from that base. To address the low-level feature extraction issue and overcome the shortcomings of traditional convolution such as too many parameters or limited receptive field, some novel convolution units have been proposed in the literature. In this article, we propose two elaborately designed and portable yet effective convolution units, i.e., directional convolution (DC) and large field convolution (LFC), combined as the extractor of low-level semantic features. DC is designed to extract directional features from specific directions, and LFC can achieve a large receptive field with few parameters. Experimental results on two public datasets provide evidence that our convolution units can help deep learning networks improve performance stably and comprehensively compared to the baseline networks.