An Efficient Semantic Segmentation Method for Remote-Sensing Imagery Using Improved Coordinate Attention
Source abstract
Abstract
Semantic segmentation stands as a prominent domain within remote sensing that is currently garnering significant attention. This paper introduces a pioneering semantic segmentation model based on TransUNet architecture with improved coordinate attention for remote-sensing imagery. It is composed of an encoding stage and a decoding stage. Notably, an enhanced and improved coordinate attention module is employed by integrating two pooling methods to generate weights. Subsequently, the feature map undergoes reweighting to accentuate foreground information and suppress background information. To address the issue of time complexity, this paper introduces an improvement to the transformer model by sparsifying the attention matrix. This reduces the computing expense of calculating attention, making the model more efficient. Additionally, the paper uses a combined loss function that is designed to enhance the training performance of the model. The experimental results conducted on three public datasets manifest the efficiency of the proposed method. The results indicate that it excels in delivering outstanding performance for semantic segmentation tasks pertaining to remote-sensing images.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Engineering, Shenyang University, Shenyang 110044, China
Northeast Geological S&T Innovation Center of China Geological Survey, Shenyang 110034, China
Key Laboratory of Black Soil Evolution and Ecological Effect, Ministry of Natural Resources, Shenyang 110034, China
Institute of Carbon Neutrality Technology and Policy, Shenyang University, Shenyang 110044, China
Funding
National Natural Science Foundation of China · Northeast Geological S&T Innovation Center of China Geological Survey · China Postdoctoral Science Foundation · Basic Scientific Research Project of the Higher Education Institutions of Liaoning Province
References (43)
Show source-provided references
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