Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000

Báscones, Daniel; González, Carlos Villaseca; Mozos, Daniel

doi:10.3390/rs10060907

Cited by 60 publications

(33 citation statements)

References 17 publications

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“…Algorithms based on VQ have high complexity, so the principal objective of the method is to develop an efficient algorithm that has fast execution. 56 State-of-the-art algorithms in the field are vector quantization principal component analysis (VQPCA) 57 and online learning dictionary. 58 Technique.…”

Section: Vector Quantizationmentioning

confidence: 99%

Comprehensive review of hyperspectral image compression algorithms

2020

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Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives.

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Section: Vector Quantizationmentioning

confidence: 99%

Comprehensive review of hyperspectral image compression algorithms

2020

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“…In [10], for the targeted brain surgery application of HS data PCA parallelization technique is evolved in minimizing the time for capturing the frame-per minute. The vector quantization and PCA technique are combined to de-correlate the process of spectral information to compress the HS image ineffective way [11]. The detection of fault or damage in structures the wind turbine will be identified using the PCA method.…”

Section: A Principal Component Analysis (Pca)mentioning

confidence: 99%

The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

Nandhini¹,

Porkodi²

2019

IJEAT

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Recent advancements in the imaging spectrometer collect both spatial and spectral information which creates a huge dimensionality. The heavy spectral information creates to build a classifier for discerning between the materials in the scene. The minimum number of training labels always in an exchange between the spectral information and the performance is called the Hughes effect. Also the redundant of spectral information and noisy data presents in the hyperspectral scene. The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality. This paper proposes the novel fusion Gravitational Mass Weighted Principal Component Analysis (GMWPCA) techniques for hyperspectral data dimensionality. Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian Pines hyperspectral dataset by employing PCA, Probability PCA, LDA, and proposed techniques. The 93.63 % high accuracy achieved by using a novel proposed method.

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“…GPUs have small volume and moderate price, providing floating point calculations and highly intensive computation capacity. Signal and image processing for remotely sensed data are an active area of research encompassing dimensionality reduction [6][7][8][9], features extraction [10,11] and compression [12], all tools that are now a firm part of big data science.…”

Section: Introductionmentioning

confidence: 99%

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

et al. 2020

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We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous system architecture) that bring together integrated central processing (CPUs) and graphics processor (GPUs) units. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or making extremely difficult) code re-usability across different platforms. In order to address this issue, we implement a multi option NonLocal Means algorithm developed using the Open Computing Language (OpenCL) applied to Hyperion hyperspectral images. Experimental results demonstrate the dramatic speed-up reached by the algorithm on GPU with respect to conventional serial algorithms on CPU and portability across different platforms. This makes accurate real time denoising of hyperspectral images feasible.

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Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000

Cited by 60 publications

References 17 publications

Comprehensive review of hyperspectral image compression algorithms

Comprehensive review of hyperspectral image compression algorithms

The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

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