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Open AccessArticle

A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

1
Department of Geo-Informatics, Central South University, Changsha 410083, China
2
Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 464; https://doi.org/10.3390/ijgi8100464
Received: 11 September 2019 / Revised: 8 October 2019 / Accepted: 20 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects. View Full-Text
Keywords: remotely sensed imagery; entropy; spatial dependence; spatial structure; multilevel geometrical mapping entropy (MGME); standard deviation (Std) remotely sensed imagery; entropy; spatial dependence; spatial structure; multilevel geometrical mapping entropy (MGME); standard deviation (Std)
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Fang, S.; Zhou, X.; Zhang, J. A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery. ISPRS Int. J. Geo-Inf. 2019, 8, 464.

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