MFALNet: A Multiscale Feature Aggregation Lightweight Network for Semantic Segmentation of High-Resolution Remote Sensing Images

Abstract

Semantic segmentation labels each pixel in high-resolution remote sensing (HRRS) images with a category. To tackle with the large size and complexity of HRRS images, this letter presents a novel multiscale feature aggregation lightweight network (MFALNet) for semantic segmentation. Unlike standard convolution, asymmetric depth-wise separable convolution residual (ADCR) unit is used to reduce the parameter size of the network and makes the optimized structure deeper but lightweight and less complex. The proposed network is an encoder–decoder structure, where multiscale feature aggregation is implemented in both the encoder and the decoder. The spatial self-attention block helps to capture long-range contextual information, and the gated convolution modules are further used for refining features when aggerating high- and low-level feature maps in the decoder. The proposed MFALNet has evaluated on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2-D semantic labeling contest open benchmark data set, and the experimental results prove that the scheme can obtain a better tradeoff between segmentation accuracy and computational efficiency compared with the state-of-the-art semantic segmentation models.