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Báo cáo hóa học: " Classification by diagnosing all absorption features (CDAF) for the most abundant minerals in airborne hyperspectral images"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Classification by diagnosing all absorption features (CDAF) for the most abundant minerals in airborne hyperspectral images | Mobasheri and Ghamary-Asl EURASIP Journal on Advances in Signal Processing 2011 2011 102 EURASIP Journal on http asp.eurasipjournals.eom content 2011 1 102 Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Classification by diagnosing all absorption features CDAF for the most abundant minerals in airborne hyperspectral images Mohammad Reza Mobasheri and Mohsen Ghamary-Asl Abstract Imaging through hyperspectral technology is a powerful tool that can be used to spectrally identify and spatially map materials based on their specific absorption characteristics in electromagnetic spectrum. A robust method called Tetracorder has shown its effectiveness at material identification and mapping using a set of algorithms within an expert system decision-making framework. In this study using some stages of Tetracorder a technique called classification by diagnosing all absorption features CDAF is introduced. This technique enables one to assign a class to the most abundant mineral in each pixel with high accuracy. The technique is based on the derivation of information from reflectance spectra of the image. This can be done through extraction of spectral absorption features of any minerals from their respected laboratory-measured reflectance spectra and comparing it with those extracted from the pixels in the image. The CDAF technique has been executed on the AVIRIS image where the results show an overall accuracy of better than 96 . Keywords remote sensing hyperspectral minerals classification absorption features 1. Introduction The image classifications are based entirely on the spectral signatures of the land cover types. This area of specialty has attracted the attention of remote sensing researchers in recent years and as a result the techniques of classification have been improved considerably. These techniques have been divided into two general categories supervised and unsupervised. In supervised classification usually the statistical methods 1