A novel binary tree support vector machine for hyperspectral remote sensing image classification

作者：Du, Peijun^*; Tan, Kun; Xing, Xiaoshi

来源：Optics Communications, 2012, 285(13-14): 3054-3060.

DOI：10.1016/j.optcom.2012.02.092

• Support vector machine (SVM)
• Hyperspectral remote sensing
• Classification
• Binary tree
• J-M distance

According to the principle of support vector machine (SVM) and the inter-class separability rule of hyperspectral data, a novel binary tree SVM classifier based on separability measure among different classes is proposed for hyperspectral image classification. J-M distance is used to measure the separability in order to generate the binary tree automatically. By experiments using airborne operational modular imaging spectrometer II (OMIS II) data, satellite EO-1 Hyperion hyperspectral data and airborne AVIRIS data, the classification accuracy of different multi-class SVMs is obtained and compared. Experimental results indicate that the proposed adaptive binary tree classifier outperforms other existing multi-class SVM strategies. Use of the adaptive binary tree SVM classifier is a novel approach to improve the accuracy of hyperspectral image classification and expand the possibilities for interpretation and application of hyperspectral remote sensing image.

• 出版日期2012-6-15
• 单位中国矿业大学（北京）; 南京大学