Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images

TL;DR

This paper introduces a novel end-to-end instance segmentation model for remote sensing images that uses semantic attention and scale complementarity to improve accuracy across varying scales and complex backgrounds.

Contribution

The proposed model combines a semantic attention module with a scale complementary mask branch, enhancing instance activation and multi-scale feature utilization for remote sensing image segmentation.

Findings

Achieved promising performance on iSAID dataset.

Effectively handles scale variability and background complexity.

Outperforms baseline methods in accuracy.

Abstract

In this paper, we focus on the challenging multicategory instance segmentation problem in remote sensing images (RSIs), which aims at predicting the categories of all instances and localizing them with pixel-level masks. Although many landmark frameworks have demonstrated promising performance in instance segmentation, the complexity in the background and scale variability instances still remain challenging for instance segmentation of RSIs. To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB). The SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map and reduce the background noise's interference. To handle the under-segmentation of geospatial instances with large varying scales, we design the SCMB that extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales to sufficiently leverage the multi-scale information. We conduct comprehensive experiments to evaluate the effectiveness of our proposed method on the iSAID dataset and the NWPU Instance Segmentation dataset and achieve promising performance.