Hyperspectral Images Sub-Pixel Classification Based on Pixel-Swapping Algorithm Extension and Its Evaluation

The capability of the matter identification is developed considerably in hyperspectral images. The spectral reflectance of surfaces in these imaging systems in the visible and near infrared range of the electromagnetic spectrum is recorded in extremely narrow and continuous bands. But for some reasons, such as existence the mixed pixels and low spatial resolution of these images, is difficult to land cover accurate position identify. The soft classification methods provide the estimation of the membership value of various classes within mixed pixels. But, by using these methods, the matter information extraction is possible only and position information extraction in sub-pixel level is impossible. In recent years, in order to solve this problem, some methods that are called SRM, have been developed for positioning the extracted membership values by soft classification process in sub-pixels for producing a higher spatial resolution land use map. In this paper, pixel-swapping method is used as the latest SRM algorithms, and with repetition the binary case of this algorithm for each class, this algorithm has been generalized and developed for multi-class. Another main point in sub-pixel classification is the performance evaluation of these classifiers. Because of the influence of various parameters in the sub-pixel classification, the evaluation of this process is very complex. Hence, as a main and innovative activity in this paper, the Influence of the neighborhood level and the zoom factor as two important parameters in the extension pixel-swapping method has been simulated and analyzed. For this purpose, in this paper a framework for evaluating the sub-pixel classification performance based on dependent on and independent on soft classification error is proposed.