A GRAPH BASED MODEL FOR SUB-PIXEL OBJECTS RECOGNITION

In this paper we tackle the problem of data analysis on high dimensional space of hyperspectral images. The remote sensing data analysis is a complex task due to many factors such as the large spectral and spatial variability. In fact, several approaches suffer from the pixel-mixed problem since mixed pixels are often sources of uncertainty and inaccuracy. Although spectral un-mixing techniques can provide abundance fractions for each class in mixed pixels, spatial distribution of these classes remains still unknown. Sub-pixel mapping techniques provide any solutions to the above-mentioned problem. Nevertheless, most of the proposed methods treat one particular type of objects because they assume that mixed pixels in a single image are identical. Sub-pixel mapping methods employing zonal objects are based on the spatial dependence assumption; the same assumption is used for linear objects while considering objects direction. Whereas, in case of encapsulated objects no spatial correlation is applied. As a result, each type of object requires special treatment, hence, the need for object recognition at sub-pixel scales. In this paper to improve sub-pixel object recognition accuracy in coarse spatial resolution image, we develop in this paper a spectro-spatial method based on a graph model, which discriminates between each type of sub-pixel object. The latter is classified into three categories: zonal, linear and small encapsulated objects based on their spatial information in neighboring pixels and their spectral information. In order to determine the suitability and the effectiveness of the proposed approach, experiments are performed with a simulated and a real hyperspectral image.