Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images

Abstract

Salient Object Detection (SOD), an important preprocessing part of image processing, identifies and labels the most attention-grabbing objects by simulating human vision. Because remote sensing images (RSIs) have different characteristics from natural images such as the limitation of shooting angle and variable scale. RSI-SOD often faces problems such as incomplete structure, missing semantic information, and blurred edges. Our Multilevel Complementary Cooperative Network (MCoCoNet) is capable of balancing semantic and detailed information to reduce noise interference to ensure semantic integrity through feature fusion in a multi-channel aligned manner. And it is adapted to the network requirements for more targeted feature extraction. Specifically, the Neighbourhood Feature Co-Extractor (NFCoE) is designed between the encoder and the decoder to utilize features from neighbouring layers to complement the missing semantic information as well as the detail information within the current layer, thus ensuring the integrity of the structure. The Parallel Refinement Block (PRB), as a decoder, which is combined with contextual information to gradually refine the target edges. It is shown by extensive experiments and visualisations that MCoCoNet provides new improvement ideas for existing RSI-SOD models.