首页|Image segmentation and knowledge graph based prototype for ground object interpretation on coastal high resolution remote sensing imagery
Image segmentation and knowledge graph based prototype for ground object interpretation on coastal high resolution remote sensing imagery
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NETL
NSTL
Taylor & Francis
ABSTRACT Recently, the methodologies for analysing remote sensing images have been iteratively refined, evolving from pixel-by-pixel analytical paradigms to Geographic Object-Based Image Analysis. Nonetheless, there remains an area for advancement concerning the transparency of the interpretation process and the rational incorporation of domain knowledge. This study introduces a framework, which hinges on image segmentation and knowledge graph, leveraging a scoring mechanism and a thresholding approach to recognize target objects. It computes the scores for all segmented patches, considering attribute weights, and classifies them as target or non-target objects based on their score thresholds. To validate the proposed framework, this paper conducted an experimental evaluation using 10 GF-2 remote sensing images from Zhejiang Province. These images were partitioned into sample and test sets. The sample image segmentation outcomes were utilized to derive attribute weights and score thresholds for eight object categories. Some object datasets, such as WHU-RS19, complemented the aforementioned data. These data elements were employed to formulate decision rules for the specified object categories. Subsequently, 10 image slices were extracted from the test images, and the above eight categories within these slices were interpreted according to the established decision rules. The interpretation outcomes revealed that both recall and precision for the eight target object categories exceeded 90%. Additionally, the overall accuracy for the interpretation of all patches was notably high at 0.88, with a Kappa coefficient of 0.99. The outcomes are also compared with the outcomes using eCognition software, which is found to be superior to eCognition’s interpretation results. The analysis of these evaluation results concluded that the proposed method enables high-precision interpretation with minimal dataset requirements, while ensuring transparency in the interpretation process.
Knowledge graphremote sensing imageinterpretationcoastal zone regionsegmentationGEOBIA
Zilu Wang、Jianyu Chen
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Shanghai JiaoTong University||Ministry of Natural Resources
NETL
NSTL
Taylor & Francis
