Semisupervised Dimensionality Reduction of Hyperspectral Images via Local Scaling Cut Criterion

2017 25th European Signal Processing Conference (EUSIPCO)

2017

Abstract-In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as 'L1-Scaling Cut' (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm. Furthermore, an iterative algorithm is described to solve the optimization problem. The experimental results of the HSI classification confirm the effectiveness of the proposed L1-SC method on both noisy and noiseless data.

Section: Experimental Results and Analysismentioning

confidence: 99%

Graph scaling cut with L1-norm for classification of hyperspectral images

2017 25th European Signal Processing Conference (EUSIPCO)

2017

“…The wide range of spectral bands of HSI data carries a wealth of information about the surface. Hence, the conventional hyperspectral classification systems solely concentrate on the spectral features of a pixel by ignoring the spatial neighborhood information [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification

IEEE Geosci. Remote Sensing Lett.

2019

Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminative information and reduce the computational burden of hyperspectral image (HSI) classification. However, the DR methods face many challenges due to limited training samples with high dimensional spectra. To address this issue, a graph-based spatial and spectral regularized local scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the proposed method is to utilize the information from both the spectral and spatial domains to achieve better classification accuracy than its spectral domain counterpart. In SSRLSC, a guided filter is initially used to smoothen and homogenize the pixels of the HSI data in order to preserve the pixel consistency. This is followed by generation of between-class and within-class dissimilarity matrices in both spectral and spatial domains by regularized local scaling cut (RLSC) and neighboring pixel local scaling cut (NPLSC) respectively. Finally, we obtain the projection matrix by optimizing the updated spatial-spectral between-class and total-class dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with two popular real-world HSI datasets.Index Terms-Dimensionality reduction, hyperspectral imaging, neighboring pixel local scaling cut, regularized local scaling cut, spatial spectral method.

“…The supervised DR approaches use the label information to learn the discriminative projections. This includes, linear discriminant analysis (LDA) [12], [13], scaling cut (SC) [14], local scaling cut (LSC) [15], [16], [14], linear discriminant embedding (LDE) [17], local fisher discriminant analysis (LFDA) [18], [19], nonparametric weighted feature extraction (NWFE) [20] and so on. The LDA seeks discriminative projection by maximizing between-class scatter and minimizing withinclass scatter by assuming the class distribution as unimodal Gaussian distribution with equal covariance.…”

Section: Introductionmentioning

confidence: 99%

A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images

IEEE Trans. Geosci. Remote Sensing

2019

Hyperspectral images (HSI) contain a wealth of information over hundreds of contiguous spectral bands, making it possible to classify materials through subtle spectral discrepancies. However, the classification of this rich spectral information is accompanied by the challenges like high dimensionality, singularity, limited training samples, lack of labeled data samples, heteroscedasticity and nonlinearity. To address these challenges, we propose a semi-supervised graph based dimensionality reduction method named 'semi-supervised spatial spectral regularized manifold local scaling cut' (S3RMLSC). The underlying idea of the proposed method is to exploit the limited labeled information from both the spectral and spatial domains along with the abundant unlabeled samples to facilitate the classification task by retaining the original distribution of the data. In S3RMLSC, a hierarchical guided filter (HGF) is initially used to smoothen the pixels of the HSI data to preserve the spatial pixel consistency. This step is followed by the construction of linear patches from the nonlinear manifold by using the maximal linear patch (MLP) criterion. Then the inter-patch and intra-patch dissimilarity matrices are constructed in both spectral and spatial domains by regularized manifold local scaling cut (RMLSC) and neighboring pixel manifold local scaling cut (NPMLSC) respectively. Finally, we obtain the projection matrix by optimizing the updated semi-supervised spatial-spectral between-patch and total-patch dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with publicly available real-world HSI datasets.Index Terms-Dimensionality reduction, hyperspectral image, manifold local scaling cut, neighboring pixel manifold local scaling cut, regularized manifold local scaling cut, semi-supervised spatial-spectral regularized manifold local scaling cut.