Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multispectral imagery. It aims to identify a set of reference spectra named endmembers that can be used to model the spectral response for each pixel of the remote image. Thus, the modelling is usually carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for large hyperspectral dataset subpixel analysis, few methods are available in the literature about the optimal selection of endmembers through field spectroscopy as well as the applied regression analysis techniques over the model obtained. This work has as main objective to deal with these aspects. With regard to the first subject mentioned and in order to determine not only specific conditions about covers (health, contamination, geographic and geologic characteristics, etc.), but to assure an efficient sampling method, ground-truth data collection and description is still an essential task. In particular there is a very important question to improve: the determination of the samples number to pick up in terms of the vegetation types. On this way, a useful statistic, based on t-Student distribution will be discussed in this paper.
The main purpose of this work is to develop a new technique for image fusion based on the optimization of the <i>Linear Mixture Model (LMM)</i> through the algorithm known as <i>Simulated Annealing (SA)</i>. The final result given by the algorithm is a fused image <i>(FI)</i> distinguished by a high spatial and spectral resolution. The algorithm proposed is being evaluated for the multispectral images registered by the Landsat 7 ETM+ sensor. In this study, the high spatial resolution image <i>(HSRI)</i> corresponds to the panchromatic image of this sensor, with a spatial resolution of 15m and the low spatial resolution image <i>(LSRI)</i> corresponds to the spectral bands TM1, TM2, TM3, TM4, TM5 and TM7, with a spatial resolution of 30m. As a result, it has been obtained images with a spectral resolution of the 6 bands and a spatial resolution of 15 m. The improvement in the quality of the fused images has allowed the identification of new, more homogeneous spectral classes.