Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model

• URL: http://arxiv.org/abs/2502.13990v1
• Date: Wed, 19 Feb 2025 02:28:12 GMT
• Title: Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model
• Authors: Huiying Shi, Zhihong Tan, Zhihan Zhang, Hongchen Wei, Yaosi Hu, Yingxue Zhang, Zhenzhong Chen,
• Abstract summary: complexity of scenes and variations in image quality result in significant variability in the performance of semantic segmentation methods. We propose RS-SQA, an unsupervised quality assessment model for semantic segmentation based on vision language model (VLM) We show that RS-SQA significantly outperforms state-of-the-art quality assessment models.
• Score: 39.648034545050535
• License:
• Abstract: The complexity of scenes and variations in image quality result in significant variability in the performance of semantic segmentation methods of remote sensing imagery (RSI) in supervised real-world scenarios. This makes the evaluation of semantic segmentation quality in such scenarios an issue to be resolved. However, most of the existing evaluation metrics are developed based on expert-labeled object-level annotations, which are not applicable in such scenarios. To address this issue, we propose RS-SQA, an unsupervised quality assessment model for RSI semantic segmentation based on vision language model (VLM). This framework leverages a pre-trained RS VLM for semantic understanding and utilizes intermediate features from segmentation methods to extract implicit information about segmentation quality. Specifically, we introduce CLIP-RS, a large-scale pre-trained VLM trained with purified text to reduce textual noise and capture robust semantic information in the RS domain. Feature visualizations confirm that CLIP-RS can effectively differentiate between various levels of segmentation quality. Semantic features and low-level segmentation features are effectively integrated through a semantic-guided approach to enhance evaluation accuracy. To further support the development of RS semantic segmentation quality assessment, we present RS-SQED, a dedicated dataset sampled from four major RS semantic segmentation datasets and annotated with segmentation accuracy derived from the inference results of 8 representative segmentation methods. Experimental results on the established dataset demonstrate that RS-SQA significantly outperforms state-of-the-art quality assessment models. This provides essential support for predicting segmentation accuracy and high-quality semantic segmentation interpretation, offering substantial practical value.

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