基于流形学习的光学遥感图像分类

随着光学遥感图像技术的快速发展与广泛应用,对光学遥感图像的准确分类具有深远的研究意义。传统特征提取方式提取的高维特征中夹杂着许多冗余信息,分类过程可能导致过拟合现象,针对传统的线性降维算法不足以保持原始数据的内部结构,容易造成数据失真这一问题,提出基于流形学习的光学遥感图像分类算法。该算法首先提取出图像的SIFT特征,然后将流形学习运用于特征降维,最后结合支持向量机进行训练和识别。实验结果表明,在Satellite、NWPU和UCMerced实验数据中,冰川、建筑群和海滩分类精度得到了有效提高,达到85%左右;针对沙漠、岩石、水域等特殊环境遥感图像,分类精度提高了10%左右。总而言之,基于流形学习的分类算法对通过降维之后的数据能够保持在原高维空间中的拓扑结构,相似特征点能得到有效聚合,预防了"维数灾难",减少了计算量,保证了分类精度。

Optical remote sensing image classification based on manifold learning

With the rapid development and wide application of optical remote sensing image technology, accurate classification of optical remote sensing images has far reaching research significance. The high-dimensional features extracted by traditional feature extraction methods are mixed with much redundant information, and the classification process can lead to over-fitting. The traditional linear dimension reduction algorithm cannot maintain the internal structure of the original data, and is easy to cause data distortion. We propose an optical remote sensing image classification algorithm based on manifold learning. Firstly, the SIFT features of the image are extracted, and the manifold learning is applied to feature dimension reduction. Finally, the support vector machine is used for training and recognition. Experimental results show that the classification accuracy of glaciers, buildings and beaches is effectively improved on the experimental data of Satellite, NWPU and UC Merced, reaching about 85%. For remote sensing images of desert, rock and water, the classification accuracy is improved by about 10%. In summary, the data based on manifold learning can maintain the topological structure in the original high-dimensional space through the dimension reduction algorithm. Similar feature points can effectively aggregate, which prevents the "dimensional disaster", reduces the calculation amount and guarantees classification accuracy.