关键词: 主动学习, 超像素, 主成分分析（PCA）, 简单线性迭代聚类（SLIC）, 支持向量机（SVM）分类器

Abstract: A new classification method of hyperspectral remote sensing image based on SLIC and active learning is proposed. First, it extracts image texture and merges with spectral feature, gets new data, uses PCA to reduce dimension of new feature data, generates false color image by the top three principal components, then uses SLIC to obtain superpixels; second, randomly selecting superpixels to create initial training samples, spectral information mean value of all pixels in superpixels as spectral information of samples, samples’ label is the largest category of number of pixels. Then, it uses active learning to obtain SVM classifier; final, it classifies superpixels by classifier, class of each superpixel has been given to pixels in corresponding superpixel, so as to achieve the purpose of hyperspectral remote sensing image classification. Experimental results show that this method can evidently reduce the time of active learning, improve the classification results, and its OA, AA and Kappa value are significantly better than the active learning methods without SLIC.

Key words: active learning, superpixels, Principal Component Analysis（PCA）, Simple Linear Iterative Clustering（SLIC）, Support Vector Machine（SVM） classifier