Open Access

ARTICLE

Remote Sensing Image Information Granulation Transformer for Semantic Segmentation

Haoyang Tang^1,2, Kai Zeng^1,2,*

1 Faculty of Information Engineering Automation, Kunming University of Science and Technology, Kunming, 650500, China
2 Yunnan Key Laboratory of Computer Technologies Application, Kunming, 650500, China

* Corresponding Author: Kai Zeng. Email:

(This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)

Computers, Materials & Continua 2025, 84(1), 1485-1506. https://doi.org/10.32604/cmc.2025.064441

Received 20 January 2025; Accepted 17 April 2025; Issue published 09 June 2025

Abstract

Semantic segmentation provides important technical support for Land cover/land use (LCLU) research. By calculating the cosine similarity between feature vectors, transformer-based models can effectively capture the global information of high-resolution remote sensing images. However, the diversity of detailed and edge features within the same class of ground objects in high-resolution remote sensing images leads to a dispersed embedding distribution. The dispersed feature distribution enlarges feature vector angles and reduces cosine similarity, weakening the attention mechanism’s ability to identify the same class of ground objects. To address this challenge, remote sensing image information granulation transformer for semantic segmentation is proposed. The model employs adaptive granulation to extract common semantic features among objects of the same class, constructing an information granule to replace the detailed feature representation of these objects. Then, the Laplacian operator of the information granule is applied to extract the edge features of the object as represented by the information granule. In the experiments, the proposed model was validated on the Beijing Land-Use (BLU), Gaofen Image Dataset (GID), and Potsdam Dataset (PD). In particular, the model achieves 88.81% for mOA, 82.64% for mF1, and 71.50% for mIoU metrics on the GID dataset. Experimental results show that the model effectively handles high-resolution remote sensing images. Our code is available at (accessed on 16 April 2025).

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Keywords

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