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Spektral Eşleştirme Yöntemleri Kullanarak Hiperspektral Görüntülerin Seyrek Gösterim Tabanlı Sınıflandırılması

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Sparse representation based approaches have recently begun to be applied to hyperspectral images due to the performance displayed in areas such as signal and image processing and successfull results have been achieved. The joint sparse representation classifier (JSRC) model has been developed so that spatial information in the hyperspectral image can be included in the classification process. However, it is assumed that the weight ratios of all neighboring pixels in a fixed size window around the test pixel are equal in this model. Particularly, as the window size increases, the error rate will increase if it is considered that the pixels belonging to different classes will be included in the classification process. In order to solve this problem, 3SM–JSRC method utilizing 3 spectral matching methods to central test pixel and each neighbor pixel in the window and combines with JSRC is proposed. It is provided that the neighboring pixel is selected or not selected according to the data obtained from the matching methods and the threshold value.
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