Narrow Results

To efficiently improve the accuracy of hyperspectral image (HSI) classification, the spatial information is usually fused with spectral information so that the classification performance can be enhanced. In this paper, we propose a new classification method called wavelet transform-based smooth ordering (WTSO). WTSO consists of three main components: wavelet transform for feature extraction, spectral–spatial based similarity measurement, smooth ordering based 1D embedding, and construction of final classifier using interpolation scheme. Specifically, wavelet transform is first imposed to decompose the HSI signal into approximate coefficients (ACs) and details coefficients (DCs). Then, to measure the similar level of pairwise samples, a novel metric is defined on the ACs, where the spatial information serves as the prior knowledge. Next, according to the measurement results, smooth ordering is applied so that the samples are aligned in a 1D space (called 1D embedding). Finally, since the reordering samples are smooth, the labels of test samples can be recovered using the simple 1D interpolation method. In the last step, in order to reduce the bias and improve accuracy, the final classifier is constructed using multiple 1D embeddings. The use of wavelet transform in WTSO can also reduce the high dimensionality of HSI data. By converting the hight-dimensional samples into a 1D ordering sequence, WTSO can reduce the computational cost, and simultaneously perform classification for the test samples. Note that in WTSO, the smooth ordering based 1D embedding and interpolation are executed in an iterative manner. And they will be terminated after finite steps. The proposed method is experimentally demonstrated on two real HSI datasets: IndianPines and University of Pavia, achieving promising results.

Hyperspectral image (HSI) classification methods based on deep learning have demonstrated excellent performance, while these deep learning methods take a lot of time to train the parameters. In this paper, we propose a deep sparse representation (SR) network (DSRNet) without spending a lot of time training network parameters in the feature extraction stage. The contributions of this paper are three-fold. First, we introduce random dictionary into HSI classification, and solve sparse representation model under this dictionary. Second, we extend the shallow sparse representation model to the deep sparse representation model, where the SR model needs to be solved for each layer and used to extract the deep features of HSI. Finally, we investigate the classification performance of different classifiers on the deep features extracted by using DSRNet. Experimental results show that the proposed method can achieve better classification results compared with some closely related HSI classification methods and the other state-of-the-art deep learning methods.