Abstract
In this study, we propose an unsupervised classification scheme based on the Dempster-Shafer Theory (TDS) and the Dezert-Smarandache Theory (DSmT) to characterize vegetated, aquatic and mineral surfaces. From pre-processed ASTER satellite images (georeferencing, geometric correction and 15 m re-sampling), neo-channels were produced by determining the spectral indices NDVI, MNDWI and NDBaI, considered as sources of information for classification of a given pixel. NDVI is a contrast function to highlight vegetation. By account, the MNDWI makes it possible to characterize the water and the NDBaI makes it possible to recognize the mineral resources. Then, we modeled respectively the formalisms of the DST and the DSmT, these formalisms are modeling tools close to advanced probabilities based on the notions of belief and fusion functions to take into account certain imperfections (uncertainty, ignorance, etc.) encountered in the acquisition of images. In addition, the DST manages a formalism of disjunction between the sources during the DSmT simultaneously manages a disjunction and a conjunction between the sources. Next we realized the algorithms and related codes that we implemented in the MATLAB environment. Our contribution lies in taking into account the imperfections (inaccuracies and uncertainties) linked to source information through the use of mass functions based on a simple Gaussian distribution support model in order to model each focal element independently of the others and to evaluate the belonging of a pixel to a class with respect to the majority of elements representing said class. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models. As for the DSmT, it presents satisfactory results for all the models proposed. The model with the exclusion integrity constraint E∩V ∩ M = φ was selected as the best model because having, in addition to an average rate of well-graded pixels of 93.34%, a compliance rate (96, 37%) with the terrain higher than those of the other models implemented.
Highlights
IntroductionOn the spot, depending on the size of the area of a surface portion, we can observe seven (07) categories of entities: Vegetated surfaces, aquatic surfaces, mineral surfaces, vegetated and aquatic surfaces, vegetated and mineral surfaces, aquatic and mineral surfaces and vegetated, aquatic and mineral surfaces
The general objective of the study is to contribute to the unsupervised classification of satellite images by merging information by developing an unsupervised classification approach, based on the use of spectral indices, as well as theories of DempsterShafer (DST) and Dezert-Smarandache (DSmT), to characterize vegetated surfaces, aquatic surfaces and mineral surfaces
The approach used consisted first of all in packaging the ASTER satellite images under ENVI to generate the sources of information to be used for the development of the classification models; we developed four classification models based on the DST; later four other classification models were developed from the Dezert-Smarandache Theory (DSmT)
Summary
On the spot, depending on the size of the area of a surface portion, we can observe seven (07) categories of entities: Vegetated surfaces, aquatic surfaces, mineral surfaces, vegetated and aquatic surfaces, vegetated and mineral surfaces, aquatic and mineral surfaces and vegetated, aquatic and mineral surfaces The characterization of these different entities on satellite images is often vitiated by imperfections (uncertainty, inaccuracy, confusion, etc.) due to the inappropriate spatial and/or spectral characteristics of the images used. It is difficult to determine the appropriate threshold values for ideal results (Chen et al, 2006; Ji et al, 2009; Uddin et al, 2010) This leads to uncertainties and inaccuracies in the information produced by the images associated with said indices. A question arises: How to take advantage of these indices taking into account their imperfections in order to improve the characterization of the Earth's surface state? To answer this concern we propose to use information fusion to take into account and manage any imperfections related to the images associated with the indices
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