Spatially adaptive interaction network for semantic segmentation of high-resolution remote sensing images

Semantic segmentation of high-resolution remote sensing imagery is pivotal in decision-making and analysis in a wide array of sectors, including but not limited to water management, agriculture, military operations, and environmental protection. This technique offers detailed and precise feature information, facilitating an accurate imagery interpretation. Despite its importance, existing methods often fall short as they lack a mechanism for spatial location feature screening. These methods tend to treat all extracted features on an equal footing, neglecting their spatial relevance. To overcome these shortcomings, we introduce a groundbreaking approach, the Spatially Adaptive Interaction Network (SAINet), designed for dynamic feature interaction in remote sensing semantic segmentation. SAINet integrates a spatial refinement module that leverages local context information to filter spatial locations and extract prominent regions. This enhancement allows the network to concentrate on pertinent areas, thereby improving the quality of feature representation. Furthermore, we present an innovative spatial interaction module that utilizes a spatial adaptive modulation mechanism. This mechanism dynamically selects and allocates spatial position weights, fostering effective interaction between local salient areas and global information, which in turn boosts the network's segmentation performance. The adaptability of SAINet allows it to capture more informative features, leading to a significant improvement in segmentation accuracy. We have validated the effectiveness and capability of our proposed approach through experiments on widely recognized public datasets such as DeepGlobe, Vaihingen, and Potsdam.