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Volume 38 Issue 5
Oct.  2019
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Article Navigation >  CARSOLOGICA SINICA  > 2019  >  38(5): 785-794
CHEN Qiying, AN Yulun, ZHOU Xu, WU Xian, XI Shijun, HAO Xinchao. Extraction of land use information in various karst landscapes based on multiple scale-spectral differential subdivision of GF-1 images[J]. CARSOLOGICA SINICA, 2019, 38(5): 785-794. doi: 10.11932/karst20190515
Citation: CHEN Qiying, AN Yulun, ZHOU Xu, WU Xian, XI Shijun, HAO Xinchao. Extraction of land use information in various karst landscapes based on multiple scale-spectral differential subdivision of GF-1 images[J]. CARSOLOGICA SINICA, 2019, 38(5): 785-794. doi: 10.11932/karst20190515
shu

Extraction of land use information in various karst landscapes based on multiple scale-spectral differential subdivision of GF-1 images

doi: 10.11932/karst20190515

  • Institute of Geography and Environmental Science, Guizhou Normal University，Guiyang，Guizhou 550000， China/Key Laboratory of Mountain Resources and Environmental Remote Sensing of Guizhou Province，Guiyang，Guizhou 550000， China

  • Publish Date: 2019-10-25
    Abstract
  • Image segmentation is a necessary step in information extraction from high-resolution images. The accuracy of such division can directly influence the precision of remote sensing classification. This work uses multiple scale-spectral differential method to conduct the division in karst mountainous areas, thus enhances the accuracy of information extraction. Using the standard most-adjacent classification method, information of land use is extracted from divided images. The accuracy of land use information extraction is compared for only multiple-scale subdivision and multiple-scale spectral differential subdivision. Results demonstrate that (1) multiple scale-spectral difference subdivision can solve the problems of over-division and under-division. (2) Multiple scale-spectral difference subdivision is superior to only using multiple scale subdivision. (3) Multiple scale-spectral difference subdivision considers many features of images such as spectra, lamination, and shape, thus permits to enhance the accuracy of division and classification of images in karst mountainous areas.

     

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