The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W20
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 21–26, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-21-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 21–26, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-21-2019

  15 Nov 2019

15 Nov 2019

AN APPROACH FOR EVALUATING THE INFORMATION CONTENT OF REMOTE SENSING IMAGES

S. M. Fang and X. G. Zhou S. M. Fang and X. G. Zhou
  • Department Geo-Informatics, Central South University, Changsha 410083, China

Keywords: Remote sensing images, Information entropy, Neighborhood information, Scatterplot matrix, Geometrical mapping model

Abstract. Due to being affected by the rapid development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as frequently used data sources are broadly applied to our daily life. At the same time, remote sensing data collection also presents a trend of popularization. To improve the utilization efficiency and availability of the obtained diversified remote sensing data, we propose a novel evaluation method based on information theory and scatterplot mapping model, i.e., geometrical mapping entropy (GME). The goal is to construct a unified model of measurement to be much more effectively and accurately evaluate the information content and quality of remotely sensed imagery. Different experimental data are used to verify the performance of the proposed method, i.e., a group of the dataset that contains different four types of images; the other group of image data contains the images with different modalities and different imaging times (2016–05, 2017–08, 2018–04, and 2018–06). Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information. Additionally, it can also reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.