Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

Boce Chu, Feng Gao, Yingte Chai, Yu Liu, Chen Yao, Jinyong Chen, Shicheng Wang, Feng Li and Chao Zhang
Additional contact information
Boce Chu: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Feng Gao: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Yingte Chai: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Yu Liu: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Chen Yao: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Jinyong Chen: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Shicheng Wang: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Feng Li: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China
Chao Zhang: Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, China

Sustainability, 2021, vol. 13, issue 23, 1-20

Abstract: Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

Keywords: remote sensing image; filtering algorithm; individual demand; urban planning; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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