A Method for Intelligently Generating Analysis Reports Based on Multi-Source Remote Sensing Image Data

As satellite technology continues to advance, the availability of multi-source remote sensing image data has significantly increased. It is now particularly vital to extract geographic features, scene understanding, and target information that are more precise, richer, and semantically informative in an efficient and rapid manner. Nonetheless, challenges such as low efficiency in manual interpretation, difficulties in integrating information, and the slow production of analysis reports have considerably affected the processing and application of vast amounts of remote sensing image data. With the advent of deep learning, intelligent text generation technology has emerged as a research focus, yet its practical applications within the field of satellite remote sensing imagery are still relatively restricted. This paper concentrates on the requirements for automated and intelligent processing and analysis, presenting a method for generating intelligent analysis reports based on multi-source remote sensing images. It introduces a multi-source remote sensing data fusion network and a hierarchical semantic generation model (MRSDF-HSG), which automates tasks such as edge detection, contour drawing, target recognition and location, change detection, and the extraction and association of target information elements. Utilizing a target sample library and an analysis report template, it automatically produces structured report products, and introduce semantic consistency checking and human review mechanisms. Experimental results indicate that this method outperforms Faster R-CNN and PSPNet in target detection, ground object classification, and report generation. This approach deeply integrates image fusion, geographic semantic driving, and intelligent generation technologies, showcasing strong innovation potential. It can be applied to various typical scenarios, including disaster monitoring, land use planning, precision agriculture, and national defense security, offering new technical support for the automated and intelligent utilization of multi-source remote sensing image data.

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Publication Details

Published
2025-06-27
DOI
https://doi.org/10.1109/crsit66101.2025.11141647
Primary Topic
Advanced Computational Techniques and Applications
Type
article
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article

A Method for Intelligently Generating Analysis Reports Based on Multi-Source Remote Sensing Image Data

Wenhua Qiu, Huimin Guo, Huoping Ding, Li Zhou et al.
Advanced Computational Techniques and Applications
article

A Method for Intelligently Generating Analysis Reports Based on Multi-Source Remote Sensing Image Data

Wenhua Qiu, Huimin Guo, Huoping Ding, Li Zhou, Liming Wang, Qiang Li
article en

Abstract

As satellite technology continues to advance, the availability of multi-source remote sensing image data has significantly increased. It is now particularly vital to extract geographic features, scene understanding, and target information that are more precise, richer, and semantically informative in an efficient and rapid manner. Nonetheless, challenges such as low efficiency in manual interpretation, difficulties in integrating information, and the slow production of analysis reports have considerably affected the processing and application of vast amounts of remote sensing image data. With the advent of deep learning, intelligent text generation technology has emerged as a research focus, yet its practical applications within the field of satellite remote sensing imagery are still relatively restricted. This paper concentrates on the requirements for automated and intelligent processing and analysis, presenting a method for generating intelligent analysis reports based on multi-source remote sensing images. It introduces a multi-source remote sensing data fusion network and a hierarchical semantic generation model (MRSDF-HSG), which automates tasks such as edge detection, contour drawing, target recognition and location, change detection, and the extraction and association of target information elements. Utilizing a target sample library and an analysis report template, it automatically produces structured report products, and introduce semantic consistency checking and human review mechanisms. Experimental results indicate that this method outperforms Faster R-CNN and PSPNet in target detection, ground object classification, and report generation. This approach deeply integrates image fusion, geographic semantic driving, and intelligent generation technologies, showcasing strong innovation potential. It can be applied to various typical scenarios, including disaster monitoring, land use planning, precision agriculture, and national defense security, offering new technical support for the automated and intelligent utilization of multi-source remote sensing image data.

China Academy of Space Technology (CN), Star Technology and Research (United States) (US), Annoroad Gene Technology (China) (CN)
Openalex Percentile: Top 88%
Advanced Computational Techniques and Applications
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