DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis

Abstract

The huge quantity of information and the high spectral resolution of hyperspectral imagery present a challenge when performing traditional processing techniques such as classification. Dimensionality and noise reduction improves both efficiency and accuracy, while retaining essential information. Among the many dimensionality reduction methods, Independent Component Analysis (ICA) is one of the most popular techniques. However, ICA is computationally costly, and given the absence of specific criteria for component selection, constrains its application in high-dimension data analysis. To overcome this limitation, we propose a novel approach that applies Discrete Cosine Transform (DCT) as preprocessing for ICA. Our method exploits the unique capacity of DCT to pack signal energy in few low-frequency coefficients, thus reducing noise and computation time. Subsequently, ICA is applied on this reduced data to make the output components as independent as possible for subsequent hyperspectral classification. To evaluate this novel approach, the reduced data using (1) ICA without preprocessing; (2) ICA with the commonly used preprocessing techniques which is Principal Component Analysis (PCA); and (3) ICA with DCT preprocessing are tested with Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers on two real hyperspectral datasets. Experimental results in both instances indicate that data after our proposed DCT preprocessing method combined with ICA yields superior hyperspectral classification accuracy.

A Preprocessing Method for Hyperspectral Target Detection Based on Tensor Principal Component Analysis

“…First, the impact of the parameters on TD is analyzed using the synthetic data shown in Figure 1. The curve in Figure 2 shows how E (n PC ) 2 varies with n PC according to Equation (18). It can be observed that the energy of the residual part changes slowly after the 4th PC.…”

“…The background is supposed to mainly exist in the PC part, while the target is regarded to present in the residual part. Another famous method, independent component analysis (ICA), which is an unsupervised blind source separation technique has also already been used in hyperspectral data processing [18] and background separation [19]. ICA can separate endmembers apart, which enables its application in hyperspectral target detection.…”

Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

“…The GSD of Botswana is 30 m, which is the least spatial resolution over the other 5 datasets that have been employed in this study. After removing noisy and water absorption bands, there are 145 good quality bands remaining for data analysis [94,95].…”

Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification

“…The main contributions of this paper are threefold. The first contribution is the application of the spatial filter in the transform domain on the noisy data rather than discarding this part of information as usually adopted by the feature extraction-based approaches [49,50]. Thus, the proposed framework makes full use of the filtered spectral and spatial information of the hyperspectral image.…”