In order to avoid the high computational-complexity inherited in traditional texture extraction method,a new textural feature based on raw image pixel patch is proposed and it is appl ied to high reso-lution remote sensing image classification combining with support vector machine,Fi rst,the original texture extracted from local image patches are projected into the compressed sub-space using the random projection technique.Then,the texture dictionary which represents local features is learned with k-means in the compressed domain for each class.Then,the visual word map is formed by coding every texton in the samples to the nearest word in the texture dictionary,and then the histogram of the visual words map and the second moment of the words are fused as the global textural feature.At last,the global texture is put into the support vector machine to classification.The proposed method is proved to be effective for texture representation and improving accuracy by experiments on two images.%针对高分辨率遥感影像的特征提取复杂、特征维数大等问题，提出一种基于原始像素块的纹理元特征提取方法，并结合支持向量机将其应用于高分辨率遥感影像分类。首先，利用随机投影对基于原始像素灰度值的特征向量降维，得到压缩的局部纹理特征。然后，对各类纹理特性向量进行聚类，将聚类中心作为视觉词汇形成压缩纹理元字典。再将样本中的纹理元编码到纹理字典中对应距离最近的词汇，得到样本的视觉词汇图，并融合词汇统计直方图与词汇二阶矩信息作为全局的纹理表达。最后，将所得全局纹理特征作为支持向量机的输入进行分类。通过两个试验影像，验证了本文方法能够有效地表达纹理，提高分类精度。
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