scholarly journals Multimodal Brain Images Fusion using Cultural Algorithm Optimized Multispectral Features

2019 ◽  
Vol 9 (2) ◽  
pp. 982-991
Keyword(s):  
Spatial Resolution ◽  
Medical Images ◽  
Multispectral Images ◽  
Cultural Algorithm ◽  
Brain Images ◽  
Spectral Content ◽  
First Case ◽  
Remote Perception ◽  
Combine Information ◽  
Evaluation Parameters

Medical images can be acquired through different techniques (modalities), which have their own application areas; some of them provide information on the functional activity, while others contain only anatomic information. Usually, in the first case, images have low spatial resolution while in the second case have a higher resolution. However, the analysis of medical images often requires the evaluation of more than one modality; in order provide the specialist with more information for decision making as well as for the analysis and the treatment of diseases. Image fusion aims to combine information from the same sensor or different sensors, so that the image fused retain the information content of each individual image. In remote perception, when multispectral images are analyzed, it is very important to preserve the content of spectral information of each of the bands. The challenge is to obtain good quality images that allow us to extract as much amount of information possible, for which it is sometimes necessary to enhance or modify the image to improve its appearance or combine images or portions thereof to combine the information. An ideal fusion of multispectral images and the band panchromatic will result in a new series of bands with greater spatial resolution and equal spectral content. This paper proposes a PCA, DWT and cultural optimized entropy based DWT fusion with the evaluation parameters; arithmetic mean (SM), Maximum value ( ) and Minimum value ( ).

scholarly journals A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

2021 ◽  
Author(s):  
Fawad Masood ◽  
Maha Driss ◽  
Wadii Boulila ◽  
Jawad Ahmad ◽  
Sadaqat Ur Rehman ◽  
...  
Keyword(s):  
Image Encryption ◽  
Storage Systems ◽  
Medical Images ◽  
Chaotic Map ◽  
Communication Channels ◽  
Security Level ◽  
Sensitive Information ◽  
X Rays ◽  
Brain Images ◽  
Contrast Analysis

AbstractMedical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.

scholarly journals Potential of High-Resolution Pléiades Imagery to Monitor Salt Marsh Evolution After Spartina Invasion

2019 ◽  
Vol 11 (8) ◽  
pp. 968 ◽  
Author(s):  
Bárbara Proença ◽  
Frédéric Frappart ◽  
Bertrand Lubac ◽  
Vincent Marieu ◽  
Bertrand Ygorra ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Salt Marshes ◽  
Spatial Information ◽  
Aerial Photographs ◽  
Conservation Strategies ◽  
Multispectral Images ◽  
Spartina Anglica ◽  
Early Assessment ◽  
Ground Truth Data ◽  
Spartina Species

An early assessment of biological invasions is important for initiating conservation strategies. Instrumental progress in high spatial resolution (HSR) multispectral satellite sensors greatly facilitates ecosystems’ monitoring capability at an increasingly smaller scale. However, species detection is still challenging in environments characterized by a high variability of vegetation mixing along with other elements, such as water, sediment, and biofilm. In this study, we explore the potential of Pléiades HSR multispectral images to detect and monitor changes in the salt marshes of the Bay of Arcachon (SW France), after the invasion of Spartina anglica. Due to the small size of Spartina patches, the spatial and temporal monitoring of Spartina species focuses on the analysis of five multispectral images at a spatial resolution of 2 m, acquired at the study site between 2013 and 2017. To distinguish between the different types of vegetation, various techniques for land use classification were evaluated. A description and interpretation of the results are based on a set of ground truth data, including field reflectance, a drone flight, historical aerial photographs, GNSS and photographic surveys. A preliminary qualitative analysis of NDVI maps showed that a multi-temporal approach, taking into account a delayed development of species, could be successfully used to discriminate Spartina species (sp.). Then, supervised and unsupervised classifications, used for the identification of Spartina sp., were evaluated. The performance of the species identification was highly dependent on the degree of environmental noise present in the image, which is season-dependent. The accurate identification of the native Spartina was higher than 75%, a result strongly affected by intra-patch variability and, specifically, by the presence of areas with a low vegetation density. Further, for the invasive Spartina anglica, when using a supervised classifier, rather than an unsupervised one, the accuracy of the classification increases from 10% to 90%. However, both algorithms highly overestimate the areas assigned to this species. Finally, the results highlight that the identification of the invasive species is highly dependent both on the seasonal presence of itinerant biological features and the size of vegetation patches. Further, we believe that the results could be strongly improved by a coupled approach, which combines spectral and spatial information, i.e., pattern-recognition techniques.

scholarly journals Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

2020 ◽  
Vol 12 (6) ◽  
pp. 993 ◽  
Author(s):  
Chen Yi ◽  
Yong-qiang Zhao ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
Resolution Enhancement ◽  
Multispectral Image ◽  
Observation Model ◽  
Multispectral Images ◽  
Spectral Matrix ◽  
Spectral Bands ◽  
Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

Data-fusion of satellite and ground sensors for river hydro-morphodynamics monitoring

2020 ◽  
Author(s):  
Maria Nicolina Papa ◽  
Michael Nones ◽  
Carmela Cavallo ◽  
Massimiliano Gargiulo ◽  
Giuseppe Ruello
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Sampling Rate ◽  
Ecological Status ◽  
Multispectral Images ◽  
Sar Images ◽  
Shoreline Changes ◽  
Fluvial Morphology ◽  
Sentinel 2

<p>Changes in fluvial morphology, such as the migration of channels and sandbars, are driven by many factors e.g. water, woody debris and sediment discharges, vegetation and management practice. Nowadays, increased anthropic pressure and climate change are accelerating the natural morphologic dynamics. Therefore, the monitoring of river changes and the assessment of future trends are necessary for the identification of the optimal management practices, aiming at the improvement of river ecological status and the mitigation of hydraulic risk. Satellite data can provide an effective and cost-effective tool for the monitoring of river morphology and its temporal evolution.</p><p>The main idea of this work is to understand which remote sensed data, and particularly which space and time resolutions, are more adapt for the observation of sandbars evolution in relatively large rivers. To this purpose, multispectral and Synthetic Aperture Radar (SAR) archive data, with different spatial resolution, were used. Preference was given to satellite data freely available. Moreover, the observations extracted by the satellite data were compared with ground data recorded by a fixed camera.</p><p>The study case is a sandy bar (area about 0.4 km<sup>2 </sup>and maximum width about 350 m) in a lowland reach of the Po River (Italy), characterized by frequent and relevant morphological changes. The bar shoreline changes were captured by a fixed video camera, installed on a bridge and operating for almost two years (July 2017 - November 2018). To this purpose, we used: Sentinel-2 multispectral images with a spatial resolution of 10 m, Sentinel-1 SAR images with a resolution of 5 x 20 m and CosmoSkyMed SAR images with a resolution of 5 m. It is worth noting that the Sentinel data of the Copernicus Programme are freely available while the CosmoSkyMed data of the Italian Space Agency (ASI) are freely distributed for scientific purpose after the successful participation to an open call. In order to validate the results provided by Sentinel and CosmoSkyMed data, we used very high resolution multispectral images (about 50 cm).</p><p>Multispectral images are easily interpreted, but are affected by the presence of cloud cover. For instance, in this analysis, the expendable multispectral images were equal to about 50% of the total archive. On the other hand, the SAR images provide information also in the presence of clouds and at night-time, but they have the drawback of more complex processing and interpretation. The shorelines extracted from the satellite images were compared with those extracted from photographic images, taken on the same day of the satellite acquisition. Other comparisons were made between different satellite images acquired with a temporal mismatch of maximum two days.</p><p>The results of the comparisons showed that the Sentinel-1 and Sentinel-2 data were both adequate for the shoreline changes observation. Due to the higher resolution, the CosmoSkyMed data provided better results. SAR data and multispectral data allowed for automatic extraction of the bar shoreline, with different degree of processing burden. The fusion of data from different satellites gave the opportunity of highly increase the sampling rate.</p>

Combined analysis of tree rings and MODIS images to evaluate beech forest productivity along geographic gradients

2020 ◽  
Author(s):  
Luca Di Fiore ◽  
Gianluca Piovesan ◽  
Michele Baliva ◽  
Alfredo Di Filippo
Keyword(s):  
Tree Ring ◽  
Spatial Resolution ◽  
Vegetation Indices ◽  
Growing Season ◽  
Forest Productivity ◽  
Tree Cover ◽  
Multispectral Images ◽  
Continental Scale ◽  
Tree Ring Data ◽  
The Mean

<p>Remote sensing is widely used for monitoring vegetation status and ecosystem productivity. The increasing interest in connecting satellite vegetation indices to actual forest productivity has led to explore their relationship mainly at coarse spatial resolution and continental scale. The aim of this study is to find a connection and predict tree growth using medium resolution multispectral images and tree ring data for a sample of Italian and Austrian beech forests along latitudinal and altitudinal gradients. Beech tree ring data were collected and analyzed during the last 20 years, recording tree positions with a GPS device. MODIS pre-composite 250 m 16 days images (MOD13Q1) from 2000 to 2018 were first re-projected and quality checked using the MODIS quality assessment. Vegetation indices (NDVI and EVI) were extracted within a distance of 750 meters from every site centroid. Only deciduous forests (assessed by Corine Land Cover) with a dense canopy cover (assessed by Global Forest Change tree cover) were selected. Eight different phenology metrics were calculated using a threshold method and a modified one and then correlated with tree ring data (Basal Area Increment, BAI). The overall network and the relationship between metrics were characterized first with a Principal Component Analysis (PCA), and then evaluating the mean phenology, exploring its relationship with environmental variables (elevation, temperature). Last, the model for predicting BAI at every site was calculated for the period 2000-2009 using the metrics as predictors in a multiple linear regression. Two group of metrics were identified from PCA: the first is made of metrics based on dates (named DOY, e.g. start of growing season), the second on the vegetation index values (named VALUE, e.g. peak value,). BAI was modeled using as predictors the highest correlate from each of the two groups of metrics. BAI predictions for every site were generally significant: the 61% of the sites had at least one significant predictor, with a mean R-squared of 0.55 over the 41 sampled sites. DOY metrics were significantly related to altitude and temperature. Because of the wide latitudinal gradient of the study sites, mean annual temperatures showed higher correlations than the altitude with the DOY metrics. The mean growing season was longer for warm sites at low altitude. The relation between multispectral images and beech populations actual growth at medium spatial resolution is consistent even for those sites that are in complex environmental conditions, making possible to predict the annual diameter growth.</p>

scholarly journals Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization

2021 ◽  
Vol 13 (21) ◽  
pp. 4219
Author(s):  
Jian Long ◽  
Yuanxi Peng
Keyword(s):  
Response Function ◽  
Spatial Resolution ◽  
Point Spread Function ◽  
Spectral Response ◽  
Estimation Method ◽  
Hyperspectral Images ◽  
Fusion Method ◽  
Multispectral Images ◽  
Point Spread ◽  
Spread Function

The fusion of low spatial resolution hyperspectral images and high spatial resolution multispectral images in the same scenario is important for the super-resolution of hyperspectral images. The spectral response function (SRF) and the point spread function (PSF) are two crucial prior pieces of information in fusion, and most of the current algorithms need to provide these two preliminary pieces of information in advance, even for semi-blind fusion algorithms at least the SRF. This causes limitations in the application of fusion algorithms. This paper aims to solve the dependence of the fusion method on the point spread function and proposes a method to estimate the spectral response function from the images involved in the fusion to achieve blind fusion. We conducted experiments on simulated datasets Pavia University, CAVE, and the remote sensing images acquired by two spectral cameras, Sentinel 2 and Hyperion. The experimental results show that our proposed SRF estimation method can improve the PSNR value by 5 dB on average compared with other state-of-the-art SRF estimation results. The proposed blind fusion method can improve the PSNR value of fusion results by 3–15 dB compared with other blind fusion methods.

scholarly journals Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps

Tecnura ◽  
2020 ◽  
Vol 24 (66) ◽  
pp. 62-75
Author(s):  
Edwin Vargas ◽  
Kevin Arias ◽  
Fernando Rojas ◽  
Henry Arguello
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Spatial Resolution ◽  
Real Data ◽  
Spectral Unmixing ◽  
Multispectral Images ◽  
Self Similarity ◽  
Diverse Range ◽  
Learned Dictionary ◽  
Non Local

Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.

scholarly journals Geometric quality assessment of CBERS-4 MUXCAM image

2016 ◽  
Vol 4 (2) ◽  
pp. 116
Author(s):  
Thiago Statella
Keyword(s):  
Spatial Resolution ◽  
Ccd Camera ◽  
High Accuracy ◽  
Fourth Generation ◽  
Multispectral Images ◽  
Geometric Correction ◽  
Positional Accuracy ◽  
Geometric Quality ◽  
Better Than

In December 2014, Brazil and China successfully launched the CBERS-4 satellite, the fourth generation of CBERS satellites. In the payload module, the satellite carries the MUXCAM, a 20 m/pixel spatial resolution multispectral camera. The MUXCAM was built by Brazil and it is an improvement of the CCD camera on board CBERS-1, 2 and 2B satellites. In this paper the geometric quality of the MUXCAM images is analyzed. One can measure the geometric quality of the CCD sensor by calculating the positioning and the internal accuracy of the images acquired by it. The positional accuracy for the MUXCAM resulted in ~404 m whereas the internal accuracy resulted in ~30 m, better than 2 pixels. Therefore, in less rigorous applications in which a high accuracy in coordinates is not mandatory, and in which such errors can be neglected, the multispectral images acquired by MUXCAM can be used without a prior geometric correction.    

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