多特征融合的遥感图像分类

针对高分辨率遥感图像特点，提出了一种多特征融合的分类方法。该方法首先改进了原始的视觉词袋生成算法；然后，分别提取图像的视觉词袋局部特征、颜色直方图特征以及Gabor纹理特征；最后采用支持向量机进行分类，并对多特征分类结果进行自适应综合。采用一个具有2 100幅图像的大型遥感图像分类公共测试数据集进行分类实验，与仅用单一特征分类方法的最高分类精度相比，本文多特征融合的遥感影像分类方法总体平均分类精度提高了10%，表明本文提出方法是一种有效的高分辨率遥感图像分类方法

Remote Sensing Image Classification Based on Adaptive Fushion of Multiple Features

Taking the multiple features of remote sensing images into consideration, a new approach is presented to the classify remote sensing images based on the fusion of multiple features. The bag of visual words (BOVW) representation is firstly improved. Then, the BOVW feature, color histogram and Gabor texture feature are extracted from the images respectively. The classification is performed by the support vector machine classifier, and the final output is obtained through adaptively fusing the results by multiple features. The proposed method has been evaluated on a large publicly available remote sensing image dataset with 2 100 images. The experimental results have witnessed that the overall classification accuracy is boosted by 10% in comparison with the method based on the single feature which owns the highest accuracy. Comprehensive experimental results indicate that the proposed approach is effective and suitable for high-resolution remote sensing image classification.