Enhancement of Component Images of Multispectral Data by Denoising with Reference

Abramov, Sergey; Uss, Mikhail; Vozel, Benôıt; Chehdi, Kacem; Егиазарян, Карен

doi:10.3390/rs11060611

Cited by 8 publications

(5 citation statements)

References 33 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is worth recalling here that all components in RGB color images are presented as 8-bit 2D data arrays with similar dynamic ranges. However, this is not true for multispectral and hyperspectral images where dynamic ranges of component images can be considerably different [24,25]. Different dynamic range of component images, if a 3D compression is applied to a multichannel image without proper pre-processing, might cause problems [26] where a component image with the smallest dynamic range (or several component images with small dynamic range) can be severely distorted.…”

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

View full text Add to dashboard Cite

This paper concerns some aspects of lossy compression applied to six-component Landsat multispectral images where all component images have identical spatial resolution. It is shown that most component images are correlated although the cross-correlation coefficients for component images vary in rather wide limits – from 0.11 in one dataset and 0.72 in another dataset to almost unity in both cases. Compression based on discrete cosine transform is considered where 2D blocks have the size of 32×32 pixels. Embedded deblocking after decompression is applied as well. Lossy compression can be applied component-wise and in a 3D manner for spectral decorrelation of data where 6-point discrete cosine transform is applied. It is shown that, in the latter case, a significantly larger compression ratio can be achieved. The attained benefit in CR is about 30-80% compared to the component-wise compression or, alternatively, the benefit in peak signal-to-noise ratio can reach a few dB for the same compression ratio. The coders are studied for peak signal-to-noise ratio varying in the limits from about 23 dB to about 46 dB that correspond to image quality starting from annoying to practically invisible distortions. In addition, there are some image pre-processing operations that are able to further improve the compression ratio. They are image normalization and band re-ordering. All possible variants of band ordering are studied for both test images. The impact of band ordering on compressed image quality is analyzed in terms of rate/distortion curves for the 2D and 3D versions of the considered coder. It is demonstrated that, due to optimal ordering, a few % improvement of CR can be gained. However, the optimal band order for the considered multichannel images is not the same and this can cause problems in practice to be studied in the future.

show abstract

Section: особливості стиснення мультиспектральних зображень із втратамиmentioning

confidence: 99%

Особливості Стиснення Мультиспектральних Зображень Із Втратами

ZRIAKHOV,

KRYVENKO,

ЛУКІН

2024

Вісник Хмельницького національного університету. Серія: Технічн

View full text Add to dashboard Cite

show abstract

“…Прикладом можуть бути зображення, що водночас спотворені дефокусуванням та шумом або зображення з шумом, що стиснені з втратами [7]. Досить розповсюдженими є ситуації гіперспектральних зображень з компонентами як високої так і досить низької якості [8] або зображення радіолокатора із синтетичною апертурою (РСА), для яких завжди присутній спекл-шум [4,9].  О. І. Єремеєв, В. В. Лукін, К. Окарма, 2020 Враховуючи великий об'єм даних дистанційного зондування і потребу в автоматизації обробки, доцільним є контроль за допомогою певних кількісних показників як якості первинних зображень, так і змін, що вносяться в них під час обробки.…”

Section: вступunclassified

Комбінована Метрика Візуальної Якості Зображень Дистанційного Зондування На Основі Нейронної Мережі

Еремеев¹,

Okarma

2020

reks

View full text Add to dashboard Cite

The wide distribution of images of remote sensing (RS) of the Earth in various application areas makes it important to ensure the high quality of such images, which is important to identify necessary information. The complexity of the systems and the impact of various physical processes cause a significant number of distortions that lead to image corruption and possible loss of information. The use of processing methods that should reduce the impact of such factors requires control of their work, which uses quantitative indicators of visual quality. The article considers the task of creating a combined visual quality metric based on an artificial neural network (ANN), which provides high accuracy of visual quality assessment and stability of work on the noise characteristic of the RS. The problem of analysis of RS distortions is considered and the approach of using the database of test images TID2013 for verification on typical RS distortions is offered. The analysis of well-known visual quality metrics and their suitability for the estimation of such images is carried out. According to its results, it was determined that the best metrics provide the accuracy of image quality assessment for RS tasks at the level of 0.93 according to Spearman's rank-order correlation coefficient with subjective estimates of the TID2013 image database. The joint application of existing quality metrics allows eliminating the shortcomings of each of them and increasing the overall efficiency, so the article considers the problems and defines the requirements for creating a combined metric involving a neural network. A method of limiting the number of involved quality metrics with the involvement of Lasso regularization is proposed, which allows determining the most informative features (quality metrics) and simplifying the procedure of selection and reduction of their number. A study was conducted on the influence of the metric selection criterion and quantity on the accuracy of the combined metric. The influence of the structure of the neural network, the number of hidden layers, and the number of neurons in them are also analyzed. Based on the obtained results, the best implementation of ANN was selected, which with the involvement of 16 visual quality metrics allows achieving the accuracy of visual quality assessment at 0.97 according to Spearman's correlation with subjective estimates of the TID2013 database.

show abstract

“…To get around this problem, we have used component images of multispectral RS data that have high SNR with a negligible noise influence. According to analysis in [57], high SNR is observed in channel # 5 of multispectral Sentinel-2 data (freely available). This gives us a large number of test images.…”

Section: Simulated Images and Estimated Parametersmentioning

confidence: 99%

“…For the training process, we have used 100 high-quality cloudless multispectral images obtained by Sentinel-2 mission with total sizes about 5500 × 5500 pixels. Due to multispectral features of this data and different levels of distortions present in component images of multispectral data, we have chosen component images in # 5 and # 11 channels with estimated values of PSNR about 50 dB that corresponds to very low intensity of self-noise and high visual quality of images [57]. Data from the channel #5 are related to red edge wavelength that is about 700 nm and data from the channel # 11 corresponds to SWIR where the wavelength is about 1600 nm.…”

Section: Peculiarities Of Nn Trainingmentioning

confidence: 99%

NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

2019

View full text Add to dashboard Cite

Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.

show abstract

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Cited by 8 publications

References 33 publications

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Особливості Стиснення Мультиспектральних Зображень Із Втратами

Комбінована Метрика Візуальної Якості Зображень Дистанційного Зондування На Основі Нейронної Мережі

NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

Contact Info

Product

Resources

About