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- Title
国产微景一号小卫星影像的城市裸地识别应用.
- Authors
孙一鸣; 张宝钢; 吴其重; 刘奥博; 高超; 牛静; 何平
- Abstract
Low-cost microsatellites and their constellations are important directions in the development of satellite remote sensing in recent years. This is because they can effectively alleviate the questions such as the low transit frequency of a single satellite and the high networking cost of satellites. Monitoring using remote sensing satellites is an important means to obtain bare land information in the ecological field owing to its wide coverage area and immunity to man-made interference. This study carried out exploratory research on urban bare land identification using the remote sensing images of low-cost micro-satellites of MV-1 Constellation. The identification results were compared to those obtained using Landsat8 images to explore the reliability of the implication of domestic low-cost micro-satellite images in urban bare land identification. To this end, this study selected Donggang District, Rizhao City, Shandong Province as an example and developed the extraction method that combines unsupervised vegetation indices-excess green and excess red (ExG-ExR)-with the maximum likelihood method. The results are as follows. ① The panchromatic images with a resolution of 5 m that were shot by Micro-satellite No. 02 of MV-1 Constellation can clearly reflect the current status of Donggang District. They have higher resolution and perform better in capturing details of ground features. However, they lack wave band advantages over Landsat8 images. ② The images of Micro-satellite No. 02 had an overall classification accuracy of 93.3% and a Kappa coefficient of up to 0.85. Therefore, the micro-satellites of MV-1 Constellation are reliable in bare land identification to some extent. ③ The difference between the bare land area in the Donggang urban area identified using Micro-satellite No. 02 images and Landsat8 images was 1.5 percentage points. This indicates that micro-satellites of MV-1 Constellation have equivalent inversion capacity in urban bare land identification to mainstream satellites under the conditions of proper algorithms, close shooting time, and consistent geo-coordinate correction.
- Subjects
SHANDONG Sheng (China); MAXIMUM likelihood statistics; URBAN research; REMOTE sensing; MICROSPACECRAFT; CITIES &; towns; PLANT shoots
- Publication
Remote Sensing for Natural Resources, 2022, Vol 34, Issue 1, p189
- ISSN
2097-034X
- Publication type
Article
- DOI
10.6046/zrzyyg.2021056