An efficient image dataset selection method based on the greedy algorithm and spatial relationship self-optimization

doi:10.13474/j.cnki.11-2246.2025.0816

Abstract

Abstract: The massive accumulation of remote sensing data makes it particularly important to efficiently and accurately retrieve high-quality image collections that meet the application requirements of the region of interest.To address the large number of retrieved images with high overlap,which makes it difficult to meet application requirements directly,this paper proposes an efficient image dataset selection method based on a greedy algorithm and self-optimization of spatial relationships.Firstly,the image dataset is pre-filtered based on image quality and intersection relationships.Then,a block-based selection is performed using a greedy algorithm.Finally,the quality of the selection results is enhanced through an optimization strategy that integrates the greedy selection results with the topological relationships of the region of interest.The results show that the selection results of the proposed method have significantly lower redundancy and higher utilization than other methods.This method can efficiently select a high-quality image dataset that fully covers the region of interest,featuring recent imaging times,low cloud cover,high resolution,and fewer images.

Key words: remote sensing image, data screening, full coverage retrieval, greedy algorithm, multi-threading

CLC Number:

P208

DONG Siyuan, YANG Yuanwei, GAO Xianjun, TAN Meilin, DU Bin, QU Weijun, CHEN Ningsheng. An efficient image dataset selection method based on the greedy algorithm and spatial relationship self-optimization[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 100-106.

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