Regional Principal Component Analysis Network With the Rolling Guidance Filter for Classifying the Hyperspectral Images

Abstract

Because conventional PCANET approach is that the conventional PCANET performs the PCA for all the segments of all the training pixel vectors, and this does not capture the difference between different segments of the same training pixel vectors, classification accuracy is not high. This paper proposes to employ a regional principal component analysis network with the rolling guidance filter (RPCANET_RGF) for performing the hyperspectral image (HSI) classification with few training samples. Regional principal component analysis network (RPCANET) proposed in this paper performs the PCA for each segment of all training pixel vectors. Besides, the rolling guidance filter (RGF) is used to remove the spatial noise and to enhance the edges of the HSIs. Different from the conventional convolutional neural networks (CNNs), the coefficients of the filters are obtained by performing the principal component analysis (PCA) on the regional segments of HSIs. This approach is also different from the conventional principal component analysis network (PCANET). Here, different segments of the same pixel image are processed by different Filters. Since the RPCANET_RGF is a general learning method that obtains the filter coefficients directly from the HSIs, the back propagation based training is not required. Hence, the RPCANET_RGF requires a less computational power for performing the training compared to the CNN. Besides, as the RPCANET_RGF can make use of both the spectral information and the spatial information for performing the classification, the computer numerical simulation results show that the classification accuracy achieved by the RPCANET_RGF is higher than that by the conventional PCANET and other state of the art methods. INDEX TERMS Regional principal component analysis network, rolling guidance filter, hyperspectral image, classification.

The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification

“…To solve the problem related to the high dimensionality of the images and the scarcity of training samples, [37] constructs an ensemble of lightweight base models embedded with spectral feature refining modules, which can be embedded into almost any CNN model for HSI classification. A special note should be made here, even if the number of training samples is small, regional PCA networks [16] and LDA networks [32] can achieve high classification accuracy.…”