Efficient dimension reduction of hyperspectral images for big data remote sensing applications

Lee

et al. 2023

Preprint

Several important sensory physical quantities exist but are difficult to quantify, including food freshness. With the aid of a hyperspectral imaging system (HIS) and machine learning (ML), meat freshness is converted into a measurable physical quantity, i.e., freshness index (F. I.), in this study. F. I. is defined from meat fluorescence, which has a strong correlation with bacterial density, using a line-scan-type HIS stimulated at 365 nm. Combined with ML techniques, hyperspectral images are processed more efficiently. By employing linear discriminant and quadratic component analyses for hyperspectral images, F. I. can be estimated from its decision boundary after hyperspectral images are taken at an unknown freshness state. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life with the aid of home appliances and smartphones.

“…LDA are commonly used. 20,21 LDA is opted for dimensionality reduction of hyperspectral data from 11 to 2 dimensions (indicated by and in Fig. 5a).…”

Section: Among Various Algorithms Available For Dimension Reduction P...mentioning

confidence: 99%

Quantification for Food Inspection enabled by Hyperspectral Imaging System and Machine Learning

Lee

et al. 2023

Preprint

“…The data processing pipeline requires a balance in accuracy, run-time and resources usage. Therefore, another measure employed is the Reduction Efficiency Coefficient (REC) [52]. This coefficient measures the reduction performance according to classification OA and the number of features reduced, achieving values near 1 when there is a good classification combined with a high reduction of features:…”

Section: Quality Measuresmentioning

confidence: 99%

“…The lowest value is 0.8863, which is derived from NIR with LDA-SVM. Nevertheless, the κ coefficients can be grouped in categories according to the strength of agreement between the OA and the expected accuracy ensued by chance [52]. The κ values should fall in the categories [0.61, 0.80] and [0.81, 1.00], which correspond to Substantial and Almost perfect, to evaluate a classification as adequate.…”

Section: Classification Overall Accuracy and κ Coefficientmentioning

confidence: 99%

Gabor Features Extraction and Land-Cover Classification of Urban Hyperspectral Images for Remote Sensing Applications

et al. 2021

Self Cite

The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time.

“…Не стоять на місці і технології дистанційного зондування (ДЗ) з аерокосмічних носіїв (рис. 1), які вирішують велику кількість завдань у лісовому господарстві, гідрології, екологічному моніторингу, сільському господарстві [1]. Сучасні засоби ДЗ отримують величезну кількість даних завдяки наступному.…”

Section: вступunclassified

Використання Візуальних Метрик Для Аналізу Стиснення З Втратами Зашумлених Зображень

Коваленко¹,

Лукін²

2021

aktt

The subject of the article is to analyze the effectiveness of lossy image compression using a BPG encoder using visual metrics as a quality criterion. The aim is to confirm the existence of an operating point for images of varying complexity for visual quality metrics. The objectives of the paper are the following: to analyze for a set of images of varying complexity, where images are distorted by additive white Gaussian noise with different variance values, build and analyze dependencies for visual image quality metrics, provide recommendations on the choice of parameters for compression in the vicinity of the operating point. The methods used are the following: methods of mathematical statistics; methods of digital image processing. The following results were obtained. Dependencies of visual quality metrics for images of various degrees of complexity affected by noise with variance equal to 64, 100, and 196. It can be seen from the constructed dependence that a working point is present for images of medium and low complexity for both the PSNR-HVS-M and MS-SSIM metrics. Recommendations are given for choosing a parameter for compression based on the obtained dependencies. Conclusions. Scientific novelty of the obtained results is the following: for a new compression method using Better Portable Graphics (BPG), research has been conducted and the existence of an operating point for visual quality metrics has been proven, previously such studies were conducted only for the PSNR metric.The test images were distorted by additive white Gaussian noise and then compressed using the methods implemented in the BPG encoder. The images were compressed with different values of the Q parameter, which made it possible to estimate the image compression quality at different values of compression ratio. The resulting data made it possible to visualize the dependence of the visual image quality metric on the Q parameter. Based on the obtained dependencies, it can be concluded that the operating point is present both for the PSNR-HVS-M metric and for the MS-SSIM for images of medium and low complexity, it is also worth noting that, especially clearly, the operating point is noticeable at large noise variance values. As a recommendation, a formula is presented for calculating the value of the compression control parameter (for the case with the BPG encoder, it is the Q parameter) for images distorted by noise with variance varying within a wide range, on the assumption that the noise variance is a priori known or estimated with high accuracy.