Hyperspectral Image Classification With Global–Local Discriminant Analysis and Spatial–Spectral Context

Abstract

Hyperspectral images (HSIs) obtained from remote sensing contain abundant information of ground objects, and precise analysis of landcover depends on effective and efficient classification of HSIs into homogeneous ground regions. While many advanced algorithms have been developed for HSI classification, it is a challenge for an algorithm to achieve a good balance between its effectiveness and efficiency due to the high dimensionality of HSIs and insufficient labeled training samples. By taking both the rich spectral features and the spatially homogeneous property of land cover distributions, in this paper, we propose a simple and efficient yet effective method for HSI classification. First, features are extracted by using a weighted spatial–spectral and global–local discriminant analysis algorithm, which is proposed to reduce the feature dimension. Then, combining the discriminant information of neighboring pixels, we propose a spatial collaboration nearest neighbor (SC-NN) classifier to make reliable class judgment for the central one. In the SC-NN classifier, the spatially homogeneous property of landcover distribution of HSIs is utilized to effectively reduce the probability of misclassification when only a small number of training samples are available. To further address the issue of a small number of training samples, we adopt an incremental learning strategy by adding the samples with high classification confidence to the training set. Experimental results on four public datasets show that our proposed method outperforms several state-of-the-art methods with high classification accuracy.