C. Aswathy ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Abstract

This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of training samples from a well-organised dictionary matrix. The sparse vector is obtained using Basis Pursuit (BP) which is a constrained l4 minimization problem. This problem is solved by using a simple and powerful iterative algorithm known as Alternating Direction Method of Multipliers (ADMM) which significantly reduces the computational complexity of the problem and thereby speeds up the convergence. The classification accuracy is considerably improved by including efficient preprocessing techniques to remove the unwanted information (noise) present in Hyperspectral images. This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). A comparison of proposed technique with one of the convex optimization tools namely, CVX is given to exhibit the fast convergence of the former method. The experiment is performed on standard Indian Pines dataset captured using AVIRIS sensor. The potential of the proposed method is illustrated by analyzing the classification indices obtained with and without applying any preprocessing methods. With only 10% training set, an overall accuracy of 96.76% is obtained for the proposed method at a much faster rate compared to computation time taken by CVX solver.

Keywords

Alternating Direction Method of Multipliers, Basis Pursuit, Classification, Hyperspectral Denoising, Legendre Fenchel Transformation

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V. Sowmya ¹, Neethu Mohan ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Amrita Nagar, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis.

Keywords

Image Denoising, Low-pass Filter, Noise, Sparse Banded Filter

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R. Reshma ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore- 641112, Tamil Nadu, IN

Abstract

Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.

Keywords

Classification, Denoising, GURLS, Hyperspectral Image, Kernel Methods, Legendre Fenchel, LibSVM

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R. Praveena ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Noise, the unwanted information in a signal reduces the quality of signal. Hence to improve the signal quality, denoising is done. The main aim of the proposed method in this paper is to deconvolve and denoise a noisy signal by least square approach using wavelet filters. In this paper, least square approach given by Selesnick is modified by using different wavelet filters in place of second order sparse matrix applied for deconvolution and smoothing. The wavelet filters used in the proposed approach for denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The result of the proposed experiment is validated in terms of Peak Signal to Noise Ratio (PSNR). Analysis of the experiment results notify that proposed denoising based on least square using wavelet filters are comparable to the performances given by deconvolution and smoothing using the existing second order filter.

Keywords

Least Square, Peak Signal to Noise Ratio (PSNR), Signal Denoising, Wavelet Filters

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M. Swarna ¹, P. Megha ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Noise in remote sensing images (aerial and satellite) is caused due to various reasons such as atmospheric interference or lack of quality in sensors used to capture them. Removal of noise in an efficient way is a big challenge for researchers. In this paper, one dimensional signal denoising based on weighted regularized least square method is mapped to two dimensional image de noising. Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The proposed technique for image denoising based on Least Square (LS) approach is experimented on five different satellite and aerial images corrupted by gaussian noise with varying noise levels and regularization parameter lambda (λ) for different wavelet filter coefficients such as ‘haar’, ‘symlet’, ‘daubechies’ and coiflet. The effectiveness of the proposed method of image denoising is compared against the existing second order filter [based on LS] and conventional wavelet based image denoising technique based on the standard metric called Peak Signal to Noise Ratio (PSNR). Findings: From the experimental result analysis obtained it is inferred that the wavelet filters outperforms the second order filter and the conventional wavelet based image denoising. The complexity of the mathematics is low in our proposed method for image denoising. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

Keywords

Gaussian Noise, Image Denoising, Least Square, Peak Signal-to-Noise Ratio, Wavelet Filter Coefficients.

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P. V. Veena ¹, G. Remmiya Devi ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, Tamil Nadu - 641112, IN

Abstract

Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim of the proposed method is to denoise a noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional least square approach proposed by Selesnick is extended to two dimensional image denoising. In our proposed technique of least square problem formulation for image denoising, the matrix constructed using second order filter coefficients is replaced by wavelet filter coefficients. Findings: The method is experimented on standard digital images namely Lena, Cameraman, Barbara, Peppers and House. The images are subjected to different noise types such as Gaussian, Salt and Pepper and Speckle with varying noise level ranging from 0.01db to 0.5db. The wavelet filters used in the proposed approach of denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The outcome of the experiment is evaluated in terms of Peak Signal to Noise Ratio (PSNR). The analysis of the experiment results reveals that performance of the proposed method of least square based image denoising by wavelet filters are comparable to denoising using existing second order sparse matrix. Applications/Improvements: Digital images are often prone to noise; hence, proceeding with further processing of such an image requires denoising. This work can be extended in future to m-band wavelet filters.

Keywords

Image Denoising, Least Square, Peak Signal to Noise Ratio (PSNR), Wavelet Filters.

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K. Deepa Merlin Dixon ¹, Aleena Ajay ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641 112, Tamil Nadu, IN

Abstract

Remotely sensed images are subjected to various types of noises. Noise interrupts the image information; hence noise removal is one of the important pre-processing steps in every image processing applications. Since both noise and edges contain highintensity values, image denoising leads to smoothening of the edges thereby reducing the visual quality of the image. Hence, edge preserved image denoising is an ever-relevant topic. Over decades, several image denoising techniques were developed. Most of the denoising algorithms are very complex and time consuming. Background/Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The onedimensional signal denoising introduced by14 is mapped into two-dimensional image denoising. The proposed method is experimented on a set of colored aerial and satellite images. The column-wise denoising of the image is performed first, followed by row-wise denoising. The performance of the proposed method is evaluated based on the standard quality metric peak signal-to-noise ratio and computational time. Findings: From the experimental results, it is observed that the proposed method outperforms the earlier denoising methods on the basis of time and complexity. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

Keywords

Least Square, Legendre-Fenchel, Peak Signal-to-Noise Ratio, Total Variation, Wavelet.

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Parvathy G. Mol ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Hyper spectral unmixing of data has become an indispensable technique in remote sensing zone. Spectral Unmixing is defined as the source separation of a mixed pixel. The fundamental sources are termed as endmembers and percentage of the source content is known as abundances. This paper demonstrates the effect of Variational Mode Decomposition (VMD) on hyper spectral unmixing algorithms based on geometrical minimum volume approaches. The proposed method is experimented on standard hyper spectral dataset namely, cuprite. The effectiveness of the proposed method is subjected to evaluation, based on the standard quality metric namely, Root Mean Square Error (RMSE). The experimental result analysis shows that, the proposed technique enhance the performance of hyper spectral unmixing algorithms based on the geometrical minimum volume based approaches.

Keywords

Endmember Signature, Hyperspectral Imaging (HI), Hyperspectral Unmixing (HU), Variational Mode Decomposition (VMD).

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S. Moushmi ¹, V. Sowmya ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidhyapeetham, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Pan sharpening is the process of fusion of panchromatic and multispectral image to obtain an output image of high spatial and spectral resolution. It is very important for various remote sensing applications such as image segmentation studies, image classification, temporal change detection etc. The present work demonstrates the application of Empirical Wavelet Transform for the fusion of panchromatic image and multispectral image by simple average fusion rule. The Proposed method is experimented on panchromatic and multispectral images captured by high resolution earth observation satellites such as GeoEye-1, QuickBird, WorldView-2 and World View-3. The effectiveness of our proposed method is evaluated by visual perception and quantitative assessment measures. The experimental analysis shows that the proposed method performs comparable to the existing fusion algorithms such as Multi-resolution Singular Value Decomposition and Discrete Wavelet Transform.

Keywords

Discrete Wavelet Transform, Empirical Wavelet Transform, Image Fusion, Multi-resolution Singular Value Decomposition, Pan Sharpening, Quality Metrics.

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Nikhila Haridas ¹, V. Sowmya ¹, K. P. Soman ²

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, Tamil Nadu, IN
2 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, IN

Abstract

Kernel based methods have emerged as one of the most promising techniques for Hyper Spectral Image classification and has attracted extensive research efforts in recent years. This paper introduces a new kernel based framework for Hyper Spectral Image (HSI) classification using Grand Unified Regularized Least Squares (GURLS) library. The proposed work compares the performance of different kernel methods available in GURLS package with the library for Support Vector Machines namely, LIBSVM. The assessment is based on HSI classification accuracy measures and computation time. The experiment is performed on two standard Hyper Spectral datasets namely, Salinas A and Indian Pines subset captured by AVIRIS (Airborne Visible Infrared Imaging Spectrometer) sensor. From the analysis, it is observed that GURLS library is competitive to LIBSVM in terms of its prediction accuracy whereas computation time seems to favor LIBSVM. The major advantage of GURLS toolbox over LIBSVM is its simplicity, ease of use, automatic parameter selection and fast training and tuning of multi-class classifier. Moreover, GURLS package is provided with an implementation of Random Kitchen Sink algorithm, which can easily handle high dimensional Hyper Spectral Images at much lower computational cost than LIBSVM.

Keywords

Classification, GURLS, Hyper Spectral Image, Kernel Methods, LIBSVM

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