鍏抽敭璇�: 楂樺厜璋遍仴鎰�, 鐩戠潱鍒嗙被, 鏋侀檺瀛︿範鏈�, 鏀寔鍚戦噺鏈�, 鏃堕棿涓庣簿搴�

Abstract: Combining the spatial features and spectral feature of hyperspectral remote sensing image in supervised classification can effectively improve the classification time and accuracy.In this study锛宼he spatial information extraction method锛宯amed watershed transform锛寃as combined with the Extreme Learning Machine(ELM)and Support Vector Machine(SVM)methods.The classification results of the datasets with the spatial features and without the spatial features were synthetically evaluated and compared.Two hyperspectral datasets锛宼he ROSIS data of Pavia university and the Hyperion data of Okavango Delta(Botswana)锛寃ere selected to test the methods.After preprocessing锛宼he training samples were selected from the images as the reference areas for each type锛宎nd the spectral features of each type were analyzed.The two classification methods were utilized to classify the hyperspectral datasets and relevant classification results were obtained.based on the validation samples selected from the images锛宼he classification results were evaluated using the confusion matrix and the execution times.After that锛宼he spectral features and spatial features were combined to classify the data.The results show that the Extreme Learning Machine(ELM) is superior to the Support Vector Machine(SVM)in the classification time and precision锛宎nd the spatial features are introduced in the classification process锛寃hich can effectively improve the classification accuracy.

Key words: Hyperspectral remote sensing, Supervised classification, Extreme learning machine, Support vector machine, Classification time and accuracy