Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering
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DOI：
10.1109/tgrs.2013.2275613
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发表时间：
2014-06-01
影响因子：
8.2
通讯作者：
Benediktsson, Jon Atli
中科院分区：
文献类型：
--
作者：
Kang, Xudong;Li, Shutao;Benediktsson, Jon Atli
特征提取是降低高光谱图像分类计算复杂度和提高分类精度的有效方法。本文提出了一种简单但功能强大的基于图像融合和递归滤波（IFRF）的特征提取方法。首先，高光谱图像被划分成多个相邻的高光谱波段的子集。然后，在每个子集中的波段融合在一起的平均，这是最简单的图像融合方法之一。最后对融合后的波段进行变换域递归滤波，得到用于分类的特征。实验进行了不同的高光谱图像，支持向量机（SVM）作为分类器。通过使用所提出的方法，支持向量机分类器的准确性可以显着提高。此外，与其他高光谱分类方法相比，所提出的IFRF方法在分类精度和计算效率方面表现出优异的性能。
Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.