The Infrared radiation characteristic research of high temperature target has an important role in the field of target recognition and tracking, etc. Affected by atmosphere, background environment and spatial resolution, the recognition to smaller thermal targets have certain limitations. This paper used two sensitive spectral intervals to high temperature object feature and used MODTRAN5.0 quantitative analyze infrared radiation energy of the heat target in the atmosphere into the pupil of the remote sensor. And this paper adopted the statistics method to analyze the sensitivity of the radiance results of the heterotherm and allohypsic targets under the condition of a very different enrage background. Simulation results compared with the results from Line by line integral model calculating HITRAN database using FASCODE model in the given same condition. The comparison results show the method in this article can be used fast and convenient to calculate high temperature target radiance and improve the recognition of high temperature target.
Hyperspectral data, consisting of hundreds of spectral bands with a high spectral resolution, enables acquisition of continuous spectral characteristic curves, and therefore have served as a powerful tool for vegetation classification. The difficulty of using hyperspectral data is that they are usually redundant, strongly correlated and subject to Hughes phenomenon where classification accuracy increases gradually in the beginning as the number of spectral bands or dimensions increases, but decreases dramatically when the band number reaches some value. In recent years，some algorithms have been proposed to overcome the Hughes phenomenon in classification, such as selecting several bands from full bands, PCA- and MNF-based feature transformations. Up to date, however, few studies have been conducted to investigate the turning point of Hughes phenomenon (i.e., the point at which the classification accuracy begins to decline). In this paper, we firstly analyze reasons for occurrence of Hughes phenomenon, and then based on the Mahalanobis classifier, classify the ground spectrum of several grasslands which were recorded in September 2012 using FieldSpec3 spectrometer in the regions around Qinghai Lake，a important pasturing area in the north of China. Before classification, we extract features from hyperspectral data by bands selecting and PCA- based feature transformations, and In the process of classification, we analyze how the correlation coefficient between wavebands, the number of waveband channels and the number of principal components affect the classification result. The results show that Hushes phenomenon may occur when the correlation coefficient between wavebands is greater than 94%，the number of wavebands is greater than 6, or the number of principal components is greater than 6. Best classification result can be achieved (overall accuracy of grasslands 90%) if the number of wavebands equals to 3 (the band positions are 370nm, 509nm and 886nm respectively) or the number of principal components ranges from 4 to 6.