引导滤波联合局部判别嵌入的高光谱影像分类

高光谱遥感影像分类是高光谱遥感影像处理和应用的重要组成部分。然而，高光谱遥感影像具有波段数量较多和空间分辨率较高等特点，给分类任务带来一定的挑战。为了提高分类精度，充分利用影像的空间信息和像素间的局部信息，提出一种引导滤波联合局部判别嵌入的高光谱影像分类方法。首先，对高光谱遥感影像进行归一化，利用主成分分析方法实现特征提取，将提取的第一主成分影像作为引导图像；其次，采用引导滤波分别提取各波段影像的空间特征；然后，将提取的空间影像特征进行叠加，通过局部Fisher判别分析完成低维嵌入；最后，将得到的低维嵌入特征输入支持向量机分类器得到分类结果。采用Indian Pines和Pavia University两幅高光谱影像进行实验的结果表明：在分别从各类地物中随机选取10%和100个样本作为训练样本的情况下，其总体分类精度分别提高到98.28%和99.45%；对比其他相关方法，该方法能够获取更高的分类精度。该方法在低维嵌入的同时，有效利用了影像的空间信息，改善了分类效果。

Hyperspectral Image Classification Based on Guided Filtering Combined with Local Discrimination Embedding

Hyperspectral remote sensing image classification is an important part of hyperspectral remote sensing image processing and application. However, hyperspectral remote sensing image has the characteristics of large number of bands and high spatial resolution, which brings some challenges to image classification. To improve the classification accuracy of hyperspectral image and make full use of the spatial and local information, a classification method based on guided filtering combined with local discrimination embedding is proposed. Firstly, the hyperspectral remote sensing image is normalized, the feature extraction is realized by principal component analysis, and the extracted first principal component image is used as the guided image. Secondly, the spatial characteristics of each band are extracted by using the guided filter. Then, the extracted spatial image features are superimposed,and low-dimensional embedding is completed by local Fisher discriminant analysis. Finally, the embedded features are input into SVM to acquire classification results. The experimental results of two hyperspectral images, Indian Pines and Pavia University, show that compared to other relevant methods, the proposed method can obtain higher classification accuracy. When 10% and 100 samples from various ground objects were randomly selected as training samples, the overall classification accuracy increased to 98.28% and 99.45% respectively. At the same time of low dimensional embedding, the proposed method can effectively use the spatial information of images and improve the classification effect.