Band Imaging Research Articles

An Investigation of the Impact of Spatiotemporal Changes of Land Use and Land Cover and Land Surface Temperature Using RS Techniques for Selected Cities in Sri Lanka

Changes in land use and land cover (LULC) have an impact on land surface temperature (LST) locally, regionally, and globally. This research focused on the spatiotemporal dynamics of LULC and LST between 2000 and 2021 at six selected meteorological stations, belonging to the different climatic zones of Sri Lanka. The LULC were classified from Landsat ETM+ and OLI/TIRS data using the maximum likelihood classification approach, while LST was retrieved using the thermal band of both Landsat images. Resultsreveal that the built-up areas have increased in all selected locations and bare land, vegetation cover, and agricultural land have all declined in the last 20 years. In Galle and Batticaloa areas, built-up areas have grown significantly by 11.9% and 11.14% respectively, by 2021. The thermal environment spatiotemporal alterations were consistent with the urbanization trend. Since many other land use types have been converted into urban areas, the average LST in Vavuniya, Anuradhapura, Batticaloa, and Galle has been rising steadily. This showed that increasing built-up area density has played a significant role in raising LST in the study areas. The vegetation cover, agricultural land, and water bodies showed the least LST change. By examining the correlation analysis between the Normalized Difference Vegetation Index (NDVI) and LST, a somewhat positive relationship was revealed only in the areas of Galle, Batticaloa, and Hambantota. Nevertheless, the findings of this study will serve as a helpful benchmark for future landscape and urban design initiatives aimed at reducing the detrimental effects of LST on urban sustainability.

Narrow band imaging-based radiogenomics for predicting radiosensitivity in nasopharyngeal carcinoma

ObjectivesThis study aims to assess the efficacy of narrow band imaging (NBI) endoscopy in utilizing radiomics for predicting radiosensitivity in nasopharyngeal carcinoma (NPC), and to explore the associated molecular mechanisms. MaterialsThe study included 57 NPC patients who were pathologically diagnosed and underwent RNA sequencing. They were categorized into complete response (CR) and partial response (PR) groups after receiving radical concurrent chemoradiotherapy. We analyzed 267 NBI images using ResNet50 for feature extraction, obtaining 2048 radiomic features per image. Using Python for deep learning and least absolute shrinkage and selection operator for feature selection, we identified differentially expressed genes associated with radiomic features. Subsequently, we conducted enrichment analysis on these genes and validated their roles in the tumor immune microenvironment through single-cell RNA sequencing. ResultsAfter feature selection, 54 radiomic features were obtained. The machine learning algorithm constructed from these features showed that the random forest algorithm had the highest average accuracy rate of 0.909 and an area under the curve of 0.961. Correlation analysis identified 30 differential genes most closely associated with the radiomic features. Enrichment and immune infiltration analysis indicated that tumor-associated macrophages are closely related to treatment responses. Three key NBI differentially expressed immune genes (NBI-DEIGs), namely CCL8, SLC11A1, and PTGS2, were identified as regulators influencing treatment responses through macrophages. ConclusionNBI-based radiomics models introduce a novel and effective method for predicting radiosensitivity in NPC. The molecular mechanisms may involve the functional states of macrophages, as reflected by key regulatory genes.

Application of narrow band imaging in the diagnosis of pharyngeal tumors

BackgroundNarrow-band imaging (NBI) endoscopy is used in various tumor detection and is important in detecting early tumors. ObjectiveTo explore the application value of NBI endoscopy in diagnosing pharyngeal tumors. Material and methodsNinety-one patients with pharyngeal masses who attended the Department of Otorhinolaryngology, Head and Neck Surgery in Gansu Provincial Hospital from January 2023 to February 2024 were selected, and NBI and white light (WL) endoscopy were applied to examine the pharynx and the relationship between the two was observed. SPSS 25.0 software was used for statistical analysis. ResultsThe sensitivity of NBI endoscopy for diagnosing laryngeal malignant lesions was 92.0 %, the specificity was 93.0 %, the positive predictive value was 88.5 %, and the negative predictive value was 95.2 %, with a high degree of concordance between the results of NBI endoscopy and the pathology; WL endoscopy had a sensitivity of 64.0 %, a specificity of 76. 7 %, a positive predictive value of 61.5 %, and a negative predictive value of 78.6 %, with WL endoscopic findings had moderate concordance with pathology. The diagnostic accuracy of NBI endoscopy was higher than that of WL endoscopy for both benign and malignant lesions and precancerous lesions. ConclusionNBI endoscopy can detect laryngeal cancer lesions more accurately.

Multi-energy in-vivo imaging of multiple contrast agents in a mouse using MPPC-based photon-counting CT

Contrast-enhanced computed tomography (CT) is an indispensable technique in modern diagnostic medicine because it not only allows non-destructive, three-dimensional observation of the inside of the body but also provides images that highlight lesions. Recent developments in photon-counting (PC) CT systems have enabled contrast-enhanced CT scanning and quantitative evaluations, such as material discrimination and concentration estimation of contrast agents, using X-ray energy information. We established a PC-CT system that combines fast scintillators and multipixel photon counters (MPPCs) and demonstrated its performance. In this paper, we report the imaging performance of a PC-CT system using a mixture of contrast agents containing gadolinium and iodine. We performed CT imaging of a static phantom containing a mixture of the two contrast agents. The concentration of each contrast agent from the CT images in multiple energy bands were estimated. We succeeded in obtaining the concentration distributions for each contrast agent and verified the possibility of discriminating between two substances with different atomic numbers. In addition, the same procedures and analytical methods used for the static phantom were applied to the in vivo imaging of a mouse, in which gadolinium- and iodine-containing contrast agents were injected at different time intervals. We also succeeded in discriminating between the two contrast agents in vivo, making it possible to provide new imaging diagnostics such as the determination of necrotic tissues and normal blood vessels. Furthermore, we succeeded in creating three-dimensional concentration maps of gadolinium and iodine in a mouse, demonstrating the great potential of MPPC-based PC-CT for future clinical applications.

A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced.

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

Dual-Channel Mapping–Gas Column Concentration Inversion Method Based on Multispectral Imaging

Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment.

Performance of UAV-derived Normalized Difference Vegetation Index (NDVI) for Early Estimation of Rice Yield

Traditionally, the rice yield is measured after the harvest, which is too late then to revert any agronomic practices to improve yield. The aerial spectral reflectance images of crops captured by a multispectral camera mounted in an Unmanned Aerial Vehicle (UAV) are capable of quantitatively measuring the internal physiological condition of crops directly associated with their health status. The aim of this study was to evaluate the performance of UAV-multispectral image derived-NDVI as a method for estimating rice yield. Multispectral drone images including single band images were acquired from a controlled rice field at Rice Research and Development Institute (RRDI), Sri Lanka. Rice variety Bg 300 was cultivated in the Yala season under four levels of Nitrogen (N) fertilizer treatment plots. According to the regression co-relation analysis the derived NDVI values at 15 and 25 m flying heights from the rice crops at the booting stage were moderately (R2=0.65 and R2=0.67, p<0.05, respectively) associated with the actual yield. The derived NDVI values indicated the rice crop vigor at the booting stage is a useful indicator for early estimation of the rice yield prior to actual harvest.

New Herbig–Haro objects associated with embedded sources

ABSTRACT We continue to present the results of the Byurakan Narrow Band Imaging Survey (BNBIS). The main goal of this survey is to search for Herbig–Haro (HH) objects and jets in Galactic dark clouds. In this work we present the results of the search in the vicinity of infrared sources that are bright in the WISE survey and embedded in the dark clouds. The survey is performed with the 1-m Schmidt telescope of Byurakan Observatory, lately equipped with a new CCD detector, which allows to obtain one square degree images of the sky in various filters. Narrow-band filters were used to obtain H α and [S ii] images, and a medium-width filter was used for the continuum imaging. New HH flows and knots were found near six embedded IR sources, which constitutes a significant proportion of the objects observed. At least two of the newly found HH flows (HH 1226 and HH 1227) lie in isolated dark clouds, thus pointing to active star formation in these regions. Other flows are also located in detached and dense globules or filaments. The length of the HH 1228 flow is about 1 pc; it has also a molecular hydrogen counterpart of the same extension. Coordinates, charts, detailed descriptions and distance estimates are provided. The lower limits of bolometric luminosities of the source stars are typical for low-mass young stellar objects.

High efficiency independent modulation at dual-wavelength based on Pancharatnam–Berry and propagation phases

Metasurfaces capable of controlling multiple wavelengths independently have attracted broad interests these years due to their significance in multi-channel information processing applications. Previous solving strategies include spatial multiplexing or extensive searching for appropriate structures, both of which have their own disadvantageous, such as low efficiency, large computer resource requirement, or time consumption. In this paper, by combining the Pancharatnam–Berry (PB) phase and propagation phase, we propose a strategy to simplify the design complexity in a dual-wavelength metasurface system, in which two simple rectangular-shaped dielectric pillars (T1 and T2) with different aspect ratios are chosen as basic structures and crossed at the geometric center to achieve manipulation. The larger pillar T2 controls the longer wavelength through the PB phase while the smaller T1 acts as a perturbation to T2. The crossed T1&T2 is studied as a whole to tune the short wavelength. The investigations by the multipole expansion method reveal that the polarization conversion ratio of the meta-atoms is dependent on the interference of the formed multipoles. To validate the proposed strategy, a dual-wavelength achromatic metalens and a wavelength-multiplexed holographic metasurface operating at the infrared thermal imaging band are designed. Our design strategy can find widespread applications in metasurfaces where multiple objectives are required to be realized.

New Horizons and Prospects in Oral Cancer Detection

ABSTRACT Recent advancements in oral cancer detection prioritize non-invasive and minimally invasive techniques for efficient and accurate screening. This review outlines progress in methods such as narrow band imaging, fluorescence imaging, and optical coherence tomography, showing promise in early lesion detection. Biomarker detection in saliva and targeted nanoparticles enhance early diagnosis, while machine learning improves diagnostic accuracy. However, clinical validation and large-scale studies are needed for widespread adoption.

Water Body Information Extraction from Remote Sensing Images based on PSPNet

Remote sensing image has the characteristics of real-time, periodicity and wide monitoring range. It can quickly and accurately obtain water area, distribution and other information, which is of great significance to the utilization and development of water resources, agricultural irrigation, flood disaster assessment and so on. Since traditional water information extraction methods only use part of image band information, the accuracy of water information extraction is low and has certain limitations. In recent years, convolutional neural network technology has developed rapidly and achieved good results in water information extraction from remote sensing images. Therefore, in this paper, Pyramid Scene Parsing Neural Network (PSPNet) was used to extract water information from Ziyuan-3 multispectral remote sensing images, to make sample sets of water, and train the convolutional neural network model. Compared with the traditional normalized difference water index (NDWI) and support vector machine (SVM), the results show that PSPNet has the highest accuracy and the lowest misclassification rate.

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. 
 OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. 
 METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.
 RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. 
 CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

Efficacy of endoscopic surveillance for pharyngeal mucosa during endoscopic resection for pharyngeal carcinoma: a multicenter prospective study.

Since patients with pharyngeal squamous cell carcinoma (SCC) often have multiple pharyngeal lesions, evaluation of pharyngeal lesions before endoscopic resection (ER) is important. However, detailed endoscopic observation of the entire pharyngeal mucosa under conscious sedation is difficult. We examined the usefulness of endoscopic surveillance with narrow band imaging (NBI) and lugol staining for detection of pharyngeal sublesions during ER for pharyngeal SCC under general anesthesia (endoscopic surveillance during treatment; ESDT). From January 2021 through June 2022, we examined 78 patients who were diagnosed with superficial pharyngeal SCC and underwent ER. They underwent the ESDT and for patients who were diagnosed with new lesions of pharyngeal SCC or high-grade dysplasia (HGD) that were not detected in the endoscopic examination before treatment, ER were performed simultaneously for new lesions and the main lesions. The primary endpoint of this study was the detection rate of new lesions of pharyngeal SCC or HGD in the ESDT. Fifteen of the 78 patients were diagnosed as having undetected new pharyngeal lesions in the ESDT and 10 (12.8%) (95% CI 6.9-22.2%) were histopathologically confirmed to have new lesions of pharyngeal SCC or HGD. Among the 13 lesions of SCC or HGD, 8 were found by NBI observation; however, 5 were undetectable using NBI but detectable by lugol staining. All of the 13 lesions had endoscopic findings of pink color sign on lugol staining. Endoscopic surveillance for pharyngeal sublesions during ER for pharyngeal SCC is feasible and useful.

Assessing Forest-Change-Induced Carbon Storage Dynamics by Integrating GF-1 Image and Localized Allometric Growth Equations in Jiangning District, Nanjing, Eastern China (2017–2020)

Forest and its dynamics are of great significance for accurately estimating regional carbon sequestration, emissions and carbon sink capacity. In this work, an efficient framework that integrates remote sensing, deep learning and statistical modeling was proposed to extract forest change information and then derive forest carbon storage dynamics during the period 2017 to 2020 in Jiangning District, Nanjing, Eastern China. Firstly, the panchromatic band and multi-spectral bands of GF-1 images were fused by using four different methods; Secondly, an improved Mask-RCNN integrated with Swin Transformer was devised to extract forest distribution information in 2020. Finally, by using the substitution strategy of space for time in the 2017 Forest Management and Planning Inventory (FMPI) data, local carbon density allometric growth equations were fitted by coniferous forest and broad-leaved forest types and compared, and the optimal fitting was accordingly determined, followed by the measurements of forest-change-induced carbon storage dynamics. The results indicated that the improved Mask-RCNN synergizing with the Swin Transformer gained an overall accuracy of 93.9% when mapping the local forest types. The carbon storage of forest standing woods was calculated at 1,449,400 tons in 2020, increased by 14.59% relative to that of 2017. This analysis provides a technical reference for monitoring forest change and lays a data foundation for local agencies to formulate forest management policies in the process of achieving dual-carbon goals.

FASO-C: A rapid visualization technique based on optimized fusion with crossover-based atom search for multi-band imagery

Multi-band image fusion is a vital image processing technique used to combine data from multiple images or sensor bands, particularly in scenarios where a wide range of spectral information is available. Its primary objective is to create a single, comprehensive image that not only visually enhances the scene but also preserves critical details and characteristics present in each source. This paper proposes a novel image fusion technique that combines the useful features from a set of source images to provide a quick visualization of the scene. The approach is highly beneficial for multi-band images, including a large number of bands acquired by sampling the wavelength spectrum at narrow intervals. The fusion process depends on a multi-objective cost function, which optimizes the resultant fused image according to specific desired characteristics. By solving this optimization framework, an optimal set of weights is obtained using a novel Fusion-Driven Atom Search Optimization with Crossover (FASO-C) algorithm for the fusion of the image bands. Crossover operation enhances the exploitation capability of ASO, enabling the global optimal solution to be achieved quickly. To further validate the effectiveness of the proposed fusion model, various experiments were conducted to assess its performance using both subjective and objective metrics, as well as analyze its convergence.

The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation

Abstract We use the GALFORM semi-analytical galaxy formation model implemented in the Planck Millennium N-body simulation to build a mock galaxy catalogue on an observer’s past lightcone. The mass resolution of this N-body simulation is almost an order of magnitude better than in previous simulations used for this purpose, allowing us to probe fainter galaxies and hence build a more complete mock catalogue at low redshifts. The high time cadence of the simulation outputs allows us to make improved calculations of galaxy properties and positions in the mock. We test the predictions of the mock against the Physics of the Accelerating Universe Survey, a narrow band imaging survey with highly accurate and precise photometric redshifts, which probes the galaxy population over a lookback time of 8 billion years. We compare the model against the observed number counts, redshift distribution and evolution of the observed colours and find good agreement; these statistics avoid the need for model-dependent processing of the observations. The model produces red and blue populations that have similar median colours to the observations. However, the bimodality of galaxy colours in the model is stronger than in the observations. This bimodality is reduced on including a simple model for errors in the GALFORM photometry. We examine how the model predictions for the observed galaxy colours change when perturbing key model parameters. This exercise shows that the median colours and relative abundance of red and blue galaxies provide constraints on the strength of the feedback driven by supernovae used in the model.

Assessment of the impact of utility-scale photovoltaics on the surrounding environment in the Iberian Peninsula. Alternatives for the coexistence with agriculture

The rapid growth of photovoltaic solar energy, to achieve decarbonization, has been accompanied by increasing land occupation and the subsequent concern in the agroforestry sector. The increase in land area occupied has been of 20% in recent years, boosting solar electricity production to 5.9% of the total in Europe. This fact raises the question of the impact on vegetation greenness and moisture in the rural environment, something that has not always been considered.Image analysis is presented as one of the most effective tools to estimate the variation of vegetation greenness and moisture. For this, terrestrial images, like those from unmanned aerial vehicles, can be used; however, this limits the amount of information available and/or increases the cost. The use of satellite images in different bands is a relatively new tool that can be exploited for the analysis of solar plants impact. This work presents a new way to use Sentinel imagery to analyse the impact of utility-scale solar plants on vegetation and moisture of the surrounding areas. According to our results, a moderate decrease in weighted index for both moisture (5%) and vegetation (3%) occurred after solar plant installation. It is expected that these results can be of help for the design of new PV and agrivoltaic plants, originating the Ground-Integrated Photovoltaics (GIPV).

Structural Controls of the Expansion of Small‐Scale Artisanal Gold of Bouda Area (Kaya‐Goren Green Belt, Burkina Faso) From Remote Sensing

AbstractSmall‐scale artisanal mining is a vital sector of the economy of Burkina Faso. This activity, correctly regulated, remains essential for the population despite security issues since 2015. Exploration of new areas for gold mining in this condition is difficult but essential to continue extractive activities. It is, therefore, important to define viable corridors, with science‐based evidence for the good prospects, to support artisanal miners’ activities in a way that minimizes the environmental impact. We describe a remote sensing approach to study the structural control of expanding small‐scale artisanal gold activity at the Bouda site and define future mining corridors. This remote sensing approach can also mitigate and remedy environmental impacts. The Minimum Noise Fraction (MNF) is applied to Landsat images (7‐ETM+, 5‐TM, and 8‐OLI, respectively, for 2000, 2011, and 2022) to detect extraction sites and perform a spatiotemporal analysis of the expansion of areas affected by gold extraction. The near‐infrared and mid‐infrared bands of Landsat images significantly contribute to detecting extraction areas. A morphological study of the site over time and Principal Component Analysis (PCA) on Landsat 8‐OLI enable us to perform a lineament analysis. The expansion of mining activity is structurally controlled, as revealed by the correlation between gold occurrences and high lineament densities, especially in the dominant NE‐SW direction. These analyses suggest five potential prospecting corridors in the Bouda site.

ОСОБЛИВОСТІ СТИСНЕННЯ МУЛЬТИСПЕКТРАЛЬНИХ ЗОБРАЖЕНЬ ІЗ ВТРАТАМИ

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.