Abstract
Classification of Hyperspectral Images (HSIs) has gained attention for the past few decades. In remote sensing image classification, the labeled samples are insufficient or hard to obtain; however, the unlabeled ones are frequently rich and of a vast number. When there are no sufficient labeled samples, overfitting may occur. To resolve the overfitting issue, in this present work, we proposed a novel approach for HSI feature extraction, called robust regularized Block Low-Rank Discriminant Analysis (BLRDA), which is a robust and efficient feature extraction method to improve the HSIs’ classification accuracy with few labeled samples. To reduce the exponentially growing computational complexity of the low-rank method, we divide the entire image into blocks and implement the low-rank representation for each block respectively. Due to the symmetric matrix requirements for the regularized graph of discriminant analysis, the k-nearest neighbor is applied to handle the whole low-rank graph integrally. The low-rank representation and the kNN can maximally capture and preserve the global and local geometry of the data, respectively, and the performance of regularized discriminant analysis feature extraction can be apparently improved. Extensive experiments on multi-class hyperspectral images show that the proposed BLRDA is a very robust and efficient feature extraction method. Even with simple supervised and semi-supervised classifiers (nearest neighbor and SVM) and randomly given parameters, the feature extraction method achieves significant results with few labeled samples, which shows better performance than similar feature extraction methods.
Highlights
With the advancement of remotely-sensed hyperspectral imaging instruments, Hyperspectral Images (HSIs) have gained widespread attention throughout the globe
In the field of remote sensing classification, the confusion matrix [46] is frequently used, which is defined in the form: M = [mij]n×n, where mij denotes that the number of pixels labeled by j should belong to class i. n is the class number
We propose a novel approach for HSI feature extraction, Regularized Block Low-rank Discriminant Analysis (BLRDA)
Summary
With the advancement of remotely-sensed hyperspectral imaging instruments, Hyperspectral Images (HSIs) have gained widespread attention throughout the globe. HSI provides the comprehensive spectral information of the materials’ physical properties. This ubiquitous technique is applied in agricultural monitoring [2,3], forestry [4], ecosystem monitoring [5], mineral identification [6,7], environmental pollution monitoring [8] and urban growth analysis [9,10]. The main drawback of the machine learning approach is overfitting [14,15] due to the limited number of training data when dealing with small sample problems in highly dimensional and nonlinear cases, which is referred to as the “Hughes phenomenon” [16,17]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have