摘要
随着遥感等对地观测技术的发展,遥感图像分辨率越来越高。相比于中低分辨率遥感图像,高分辨率遥感图像能够提供更详细的地面信息,但各种地物空间结构分布较复杂。本文针对高分辨率遥感图像中不同目标的各种特征有效性不同,及彼此存在互补现象,提出一种分层多特征融合的场景分类方法。该方法首先对图像进行预分类,粗分为特征点分布均匀与不均匀两大类;然后,对分布均匀类别提取颜色直方图特征和Gabor纹理特征,对分布不均匀类别提取ScSPM(基于稀疏编码的空间金字塔匹配)特征;最后分别训练支持向量机分类器对测试图像进行分类。在一个2100幅图像构成的大型遥感图像数据库上的实验结果表明,提出的算法比仅用单一特征分类方法的最高精度提高了10%;与其他融合方法相比,提出的方法取得了最高分类精度,达到了90.1%;算法时间复杂度也大为降低。
With the development of remote sensing and the related techniques, the resolution of these images is largely improved. Compared with moderate or low resolution images, high-resolution images can provide more detailed ground information. Howev- er, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of fea- tures. The effectiveness of these features is not the same, and some of them are complementary. Considering the above character- istics, a new method is proposed to classify remote sensing images based on the hierarchical fusion of multi-feature. Firstly, these images are pre-classified into two categories in terms of whether feature points are uniform or non-uniform distributed. Then, the color histogram and Gabor texture feature are extracted from the uniform distributed categories, and the SeSPM（ linear spatial pyr- amid matching using sparse coding）feature is obtained from the non-uniform distributed categories. Finally, the classification is performed by the two different support vector machine classifiers. The experimental results on a large remote sensing image data- base with 2100 images show that the overall classification accuracy is boosted by 10% in comparison with the highest accuracy of single feature. Compared with other methods of multiple features fusion, the proposed method has achieved the highest classification accuracy which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.
出处
《计算机与现代化》
2016年第5期1-6,共6页
Computer and Modernization
基金
国家自然科学基金资助项目(61170200)
江苏省重点研发计划(社会发展)项目(BE2015707)
