# Center-guided Classifier for Semantic Segmentation of Remote Sensing Images

Source: [Center-guided Classifier for Semantic Segmentation of Remote Sensing Images](<https://www.sota2.com/research/paper/68fa77961e209131cea7427e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:22:38.768Z

Authors: Wei Zhang, Mengting Ma, Yizhen Jiang, Rongrong Lian, Zhenkai Wu, Kangning Cui, Xiaowen Ma

Published: 2025-03-21T09:21:37.000Z

## Abstract

Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing images more challenging. Moreover, existing semantic segmentation models for remote sensing images usually employ a vanilla softmax classifier, which has three drawbacks: (1) non-direct supervision for the pixel representations during training; (2) inadequate modeling ability of parametric softmax classifiers under large intraclass variance; and (3) opaque process of classification decision. In this paper, we propose a novel classifier (called CenterSeg) customized for RSI semantic segmentation, which solves the abovementioned problems with multiple prototypes, direct supervision under Grassmann manifold, and interpretability strategy. Specifically, for each class, our CenterSeg obtains local class centers by aggregating corresponding pixel features based on ground-truth masks, and generates multiple prototypes through hard attention assignment and momentum updating. In addition, we introduce the Grassmann manifold and constrain the joint embedding space of pixel features and prototypes based on two additional regularization terms. Especially, during the inference, CenterSeg can further provide interpretability to the model by restricting the prototype as a sample of the training set. Experimental results on three remote sensing segmentation datasets validate the effectiveness of the model. Besides the superior performance, CenterSeg has the advantages of simplicity, lightweight, compatibility, and interpretability. Code is available at https://github.com/xwmaxwma/rssegmentation.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.16963>)

[PDF](<https://arxiv.org/pdf/2503.16963.pdf>)