Absrroct-i n this study, targets and nontargets in a multispectral image were characterized in terms of their spectral features. Then, target detection procedures were performed. Target detection problem was considered as a nvo-class classification problem with four-band (Red-Green-Blue-Near infrared) images.For this purpose, statistical techniques were employed. These are Parallelepiped, Euclidean Distance and Maximum Likelihood (ML) algorithms, which beIong to supervised statistical classification methods. To obtain the training data belonging to each class, the training regioas were selected as polygonal. After determination of the parameters of the algorithms with the training set, classification was accomplished at each pixel as target or background. Consequently, ciassification results were displayed on thematic maps.The algorithms were trained with the same training sets, and their comparative performances were tested under various situations. During these studies, the effects of training area selection and various levels of thresholds wsere evaluated based on the efficiency of the algorithms. The selection of appropriate

The main objective of classification is to partition the surface materials into non-overlapping regions by using some decision rules. For supervised classification, the hyperspectral imagery (HSI) is compared with the reflectance spectra of the material containing similar spectral characteristic.As being a spectral similarity based classification method, prediction of different level of upper and lower spectral boundaries of all classes spectral signatures across spectral bands constitutes the basic principles of the Multi-Scale Vector Tunnel Algorithm (MS-VTA) classification algorithm. The vector tunnel (VT) scaling parameters obtained from means and standard deviations of the class references are used.In this study, MS-VT method is improved and a spectral similarity based technique referred to as Weighted Chebyshev Distance (WCD) method for the supervised classification of HSI is introduced. This is also shown to be equivalent to the use of the WCD in which the weights are chosen as an inverse power of the standard deviation per spectral band. The use of WCD measures in terms of the inverse power of standard deviations and optimization of power parameter constitute the most important side of the study.The algorithms are trained with the same kinds of training sets, and their performances are calculated for the power of the standard deviation. During these studies, various levels of the power parameters are evaluated based on the efficiency of the algorithms for choosing the best values of the weights.

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In this study, targets and nontargets in a hyperspectral image are characterized in terms of their spectral features. Target detection problem is considered as a two-class classification problem. For this purpose, a vector tunnel algorithm (VTA) is proposed. The vector tunnel is characterized only by the target class information. Then, this method is compared with Euclidean Distance (ED), Spectral Angle Map (SAM) and Support Vector Machine (SVM) algorithms. To obtain the training data belonging to target class, the training regions are selected randomly. After determination of the parameters of the algorithms with the training set, detection procedures are accomplished at each pixel as target or background. Consequently, detection results are displayed as thematic maps.The algorithms are trained with the same training sets, and their comparative performances are tested under various cases. During these studies, various levels of thresholds are evaluated based on the efficiency of the algorithms by means of Receiver Operating Characteristic Curves (ROC) as well as visually.