Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images

Abstract

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this paper, we propose a novel spatial-spectral feature extraction framework, Multiscale 2D singular spectrum analysis (2D-SSA) with principal component analysis (2D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the principal component analysis (PCA) is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatialspectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with ten state-of-the-art classifiers, including six spatial-spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands.

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

“…The hybrid methods have the following benefits: Specifically designed to overcome the limitations and take advantage of the methodologies involved in the concerned hybrid to achieve a deep, rich, and insightful conclusion (general). Addressing and resolving multiple issues regarding the handling and analyzing the HSI data, at a time, depending upon the methods that are chosen for mixing/hybridizing [ 179 – 183 ]. Coherence in time, space, and cost complexities [ 184 – 186 ].…”

“…Addressing and resolving multiple issues regarding the handling and analyzing the HSI data, at a time, depending upon the methods that are chosen for mixing/hybridizing [ 179 – 183 ].…”

Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction

“…Our proposed approach was evaluated by comparing its performance with eight state-of-the-art approaches for HSI feature extraction (Algorithm 2, see Section 3.4.5). These include SVM [7], Edge Preserving Filter (EPF) [26], superpixel-based classification via multiple kernels (SCMK) [34], region-based relaxed multiple kernel (R2MK) [35], adjacent superpixel-based multiscale generalized spatial-spectral kernel (ASMGSSK) [36], Multiscale superpixel-based PCA (MsuperPCA) [37], 2D Singular Spectrum Analysis (2D-SSA) [29], and 2D Multiscale Singular Spectrum Analysis (2D-MSSA) [31]. A common way to measure the efficiency of feature extraction is through the accuracy of the classifier scored by the experiments.…”

“…Since the optimal window size may vary depending on the dataset, ref. [31] adopts a multiscale strategy to improve the generalization ability.…”

“…2D-SSA suffers, however, from several limitations, such as reduced utilization of the abundant spectral information available in data, and over-smoothing or under-smoothing of classification results because of the fixed embedding window size. To overcome the challenge of selecting the optimal embedding window size, recently, multi-scale 2D-SSA has been proposed for the effective extraction of discriminative spatial features under different noise conditions [31,32].…”