Band Imaging Research Articles

SDSS IV MaNGA: visual morphological and statistical characterization of the DR15 sample

ABSTRACTWe present a detailed visual morphological classification for the 4614 MaNGA galaxies in SDSS Data Release 15, using image mosaics generated from a combination of r band (SDSS and deeper DESI Legacy Surveys) images and their digital post-processing. We distinguish 13 Hubble types and identify the presence of bars and bright tidal debris. After correcting the MaNGA sample for volume completeness, we calculate the morphological fractions, the bi-variate distribution of type and stellar mass M* – where we recognize a morphological transition ‘valley’ around S0a-Sa types – and the variations of the g − i colour and luminosity-weighted age over this distribution. We identified bars in 46.8 per cent of galaxies, present in all Hubble types later than S0. This fraction amounts to a factor ∼2 larger when compared with other works for samples in common. We detected 14 per cent of galaxies with tidal features, with the fraction changing with M* and morphology. For 355 galaxies, the classification was uncertain; they are visually faint, mostly of low/intermediate masses, low concentrations, and discy in nature. Our morphological classification agrees well with other works for samples in common, though some particular differences emerge, showing that our image procedures allow us to identify a wealth of added value information as compared to SDSS-based previous estimates. Based on our classification, we also propose an alternative criteria for the E–S0 separation, in the structural semimajor to semiminor axis versus bulge to total light ratio (b/a − B/T) and concentration versus semimajor to semiminor axis (C − b/a) space.

Cloud removal from satellite imagery using multispectral edge-filtered conditional generative adversarial networks

ABSTRACT We propose a Generative Adversarial Network (GAN) based architecture for removing clouds from satellite imagery. Data used for training comprises of visible light RGB and near-infrared (NIR) band images. The novelty lies in the structure of the discriminator in the GAN architecture, which compares generated and target cloud-free RGB images concatenated with their edge-filtered versions. Experimental results show that our approach to removing clouds outperforms both visually and according to metrics, a benchmark solution that does not take edge filtering into account, and that improvements are robust when varying both training dataset size and NIR cloud penetrability.

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

The developed method extracts bathymetry distributions from multiple satellite image bands. The automated remote sensing function is sparsely coded and combines spiking neural net anomaly filtration, spline, and multi-band fittings. Survey data were used to identify an activation threshold, decay rate, spline fittings, and multi-band weighting factors. Errors were computed for remotely sensed Landsat satellite images. Multi-band fittings achieved an average error of 25.3 cm. This proved sufficiently accurate to automatically extract shorelines to eliminate land areas in bathymetry mapping.

Thiersch graft follow-up with narrow band imaging for acquired atresia of the external auditory canal: Canaloplasty with Thiersch graft versus vascularization evaluated with narrow band imaging.

Acquired atresia of the external ear canal (EAC) is defined as a narrowing caused by abnormal production of soft or bony tissue. EAC has two different phases, a wet one and a dry one. A computed tomography (CT) scan may show images where the soft tissue fills the EAC. Treatment with CT may be medical and/or surgical. The surgical technique most widely adopted is canaloplasty with a skin free flap. To the best of our knowledge, no previous article has reported data analyzing the vascularization of acquired atresia of the EAC and vascularization of the skin flap during follow-up with narrow-band imaging (NBI). This paper aimed to study post-surgical vascularization of the skin graft, with NBI endoscopy, to identify any eventual areas with less perfusion that may lead to degeneration and fibrosis. Patients suffering from acquired atresia of the external auditory canal, surgically treated in the Department of Organi di Senso of Sapienza University, from 2017 to 2020 were enrolled. All patients underwent anamnestic collection, physical examination, and mastoid CT. Pre- and post-operative otoendoscopic evaluations (at 1, 3, 6 and 12 months) were performed with both cold white light (CWL) and NBI endoscopic vision. 17 patients were enrolled in the study. Pre-operative otoendoscopic examination of CWL showed stenosis with a diameter <75% and a tympanic membrane not viewable in all patients. At 12 months of follow-up, 94% of patients had no recurrence of EAC stenosis. 88% of patients presented an adequate vascularization by NBI. Our study aimed to evaluate whether the NBI endoscopic view and the analysis of vascularization may be useful for improving the prognosis of patients surgically treated with canaloplasty and Thiersch graft for acquired atresia of EAC, concerning the single analysis using CWL endoscope.

Polarization Sensitive Photothermal Mid-Infrared Spectroscopic Imaging of Human Bone Marrow Tissue.

Collagen quantity and integrity play an important role in understanding diseases such as myelofibrosis (MF). Label-free mid-infrared spectroscopic imaging (MIRSI) has the potential to quantify collagen while minimizing the subjective variance observed with conventional histopathology. Infrared (IR) spectroscopy with polarization sensitivity provides chemical information while also estimating tissue dichroism. This can potentially aid MF grading by revealing the structure and orientation of collagen fibers. Simultaneous measurement of collagen structure and biochemical properties can translate clinically into improved diagnosis and enhance our understanding of disease progression. In this paper, we present the first report of polarization-dependent spectroscopic variations in collagen from human bone marrow samples. We build on prior work with animal models and extend it to human clinical biopsies with a practical method for high-resolution chemical and structural imaging of bone marrow on clinical glass slides. This is done using a new polarization-sensitive photothermal mid-infrared spectroscopic imaging scheme that enables sample and source independent polarization control. This technology provides 0.5µm spatial resolution, enabling the identification of thin (≈1µm) collagen fibers that were not separable using Fourier Transform Infrared (FT-IR) imaging in the fingerprint region at diffraction-limited resolution ( ≈ 5µm). Finally, we propose quantitative metrics to identify fiber orientation from discrete band images (amide I and amide II) measured under three polarizations. Previous studies have used a pair of orthogonal polarization measurements, which is insufficient for clinical samples since human bone biopsies contain collagen fibers with multiple orientations. Here, we address this challenge and demonstrate that three polarization measurements are necessary to resolve orientation ambiguity in clinical bone marrow samples. This is also the first study to demonstrate the ability to spectroscopically identify thin collagen fibers (≈1µm diameter) and their orientations, which is critical for accurate grading of human bone marrow fibrosis.

Young Star-Forming Complexes in the Ring of the S0 Galaxy NGC 4324

We present the results of our study of starforming regions in the lenticular galaxy NGC 4324. During a complex analysis of multiwavelength observational data -- the narrow-band emission-line images obtained with the 2.5-m telescope at the Caucasus Mountain Observatory of the Sternberg Astronomical Institute of the Moscow State University and the archival images in the broad bands of the SDSS, GALEX and WISE surveys -- we have detected young starforming complexes (clumps) located in the inner ring of the lenticular galaxy NGC 4324, and we have established a regular pattern of their distribution along the ring, which, nevertheless, changes with time (with age of starforming regions). We suggest several possible evolutionary paths of the lenticular galaxy NGC 4324, of which the accretion of gas-rich satellites or giant clouds (the so-called minor merging) is the most probable one.

Inexpensive multispectral imaging device

Multi/hyperspectral imaging is an important technique to obtain color data and spectral monitoring. It is a precise remote sensing technique that includes two or more bands in the visible and invisible spectrum. This study demonstrates the design and construction of a low-cost, multispectral imaging system using a single linear array sensor and a varifocal lens coupled to individual spectral band-pass filters. The system allows the automated data acquisition of raw spectral data for seven spectral bands, each with different wavelengths. The raw data are visualized in real-time to generate images of spectral bands to obtain the desired spectral information on the computer graphical screen. Sensor arrays with spectral responsivity between 0.3 and 1.05 μm and motorized filter disks with a linear guide are used for acquiring data from specific wavelengths. Axis movement of the imaging device is achieved by a linear guide for spatial monitoring. Seven different bands are scanned and transferred to the computer via motorized band-pass filter sets with the imaging device. The multispectral imaging device has high accuracy with a 0.9936 linear correlation coefficient (r2) and averaged noise of the total system is below 1% (0.7%). Although the present configuration uses seven spectral bands including visible and narrow bandpass optical filters to obtain multispectral information from an object, the system is designed to have a replaceable and selectable motorized rotating filter, easily remounted, and adjusted up to forty spectral bands with 20 nm full width at half maximum (FWHM).

A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction.

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

MEDUSA: An open-source and webcam based multispectral imaging system

Multispectral imaging is at the forefront of contactless surface analysis. Standard multispectral imaging systems use sophisticated software, cameras and light filtering optics. This paper discloses the building of a customizable and cost-effective multispectral imaging and analysis system. It integrates a web camera, light emitting diodes (LEDs) lighting, a semisphere for even lightening, an open-source Arduino™ development board and a free Python application to automatically obtain and visually analyze multispectral images.The device is hereafter called MEDUSA and its optical performance was tested for repeated Imaging consistency, visible and near infrared band sensitivity and lighting evenness. Four proof of concept tests were run in order to understand the advantageous use of this system, as compared to a simple visual score of diverse samples. Each of three qualitative tests used sets of 12 LED band spectral images to analyze ink changes in a counterfeit bill, surface bruises on Hass avocado fruits and transient changes in petri dish grown bacterial colonies. A fourth test used single band imaging in a set of standard laboratory analyzed plant samples, to quantitatively relate a red band light reflectance to its nitrogen content. These tests indicate that MEDUSA made images may yield qualitative and quantitative spectral information unseen to the naked eye, suggesting potential use in currency counterfeit tests, food quality analyses, microbial phenotyping and agricultural plant chemistry.MEDUSA can be freely reproduced and customized from this research, making it a powerful and affordable analytical tool to analyze a wide range of subtle chemical properties in samples at industrial and science fields.

Role of Macroscopic Image Enhancement in Diagnosis of Non-Muscle-Invasive Bladder Cancer: An Analytical Review.

Early diagnosis of non-muscle-invasive bladder cancer (NMIBC) is of paramount importance to prevent morbidity and mortality due to bladder cancer. Although white light imaging (WLI) cystoscopy has long been considered the gold standard in the diagnosis of bladder cancer, it can miss lesions in a substantial percentage of patients and is very likely to miss carcinoma in situ and dysplasia. Tumor margin detection by WLI can be inaccurate. Moreover, WLI could, sometimes, be inadequate in distinguishing inflammation and malignancy. To improve the diagnostic efficacy of cystoscopy, various optical image enhancement modalities have been studied. These image enhancement modalities have been classified as macroscopic, microscopic, or molecular. Photodynamic diagnosis (PDD), narrow band imaging (NBI), and Storz image 1 S enhancement (formerly known as SPIES) are macroscopic image enhancement modalities. A relevant search was performed for literature describing macroscopic image enhancement modalities like PDD, NBI, and image 1 S enhancement. The advantages, limitations, and usefulness of each of these in the diagnosis of bladder cancer were studied. Photodynamic diagnosis requires intravesical instillation of a photosensitizing agent and a special blue light cystoscope system. PDD has been shown to be more sensitive than WLI in the detection of bladder cancer. It is superior to WLI in the detection of flat lesions. Bladder tumor resection (TURBT) by PDD results in more complete resection and reduced recurrence rates. PDD-guided TURBT may have some role in reducing the risk of progression. Narrow band imaging provides increased contrast between normal and abnormal tissues based on neovascularization, thereby augmenting WLI. NBI requires a special light source. There is no need for intravesical contrast instillation. NBI is superior to WLI in the detection of bladder cancer. The addition of NBI to WLI improves the detection of flat lesions like carcinoma in situ. NBI is not useful in predicting invasive tumors or grades of tumors. NBI-directed TURBT reduces recurrence rates and recurrence free survival. But its efficacy in retarding progression is unproven. Image 1 S-enhancement utilizes software-based image enhancement modes without the need for a special light source or intravesical contrast instillation. This system provides high-quality images and identifies additional abnormal-looking areas. Another advantage of this system is simultaneous side-by-side visualization of WLI and enhanced image, providing WLI images as the control for comparison. As with PDD, S-enhancement produces a lower rate of a missed bladder cancer diagnosis. The system significantly improves the diagnosis of NMIBC. The sensitivity and negative predictive value of image 1 S enhancement increase with the increase in cancer grade. A negative test by S-enhancement effectively rules out NMIBC. All the image enhancement modalities have proven their utility in improving detection and short-term cancer control. But none of these modalities have proven their utility in delaying progression, or in long-term cancer control. Cancer progression and long-term control are governed by the biological nature of cancer cells. Early detection by optical enhancement may not be of utility in this regard. Well-designed studies are needed to establish the efficacy of these modalities in the evaluation of patients with bladder cancer. The last word, in this regard, is yet to be written.

Meta-Learner Hybrid Models to Classify Hyperspectral Images

Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance.

Comparison of blue laser imaging and narrow band imaging for the differentiation of diminutive colorectal polyps: A randomized controlled trial.

To compare the diagnostic efficacy of blue laser imaging (BLI)- bright and narrow band imaging (NBI) modes of image enhanced endoscopy (IEE) in differentiating neoplastic and non-neoplastic lesions of diminutive colorectal polyps. We conducted a prospective randomized controlled trial from September 2015 to July 2016. The participants were randomly assigned (1:1) for colonoscopy with polyp classification under NBI or BLI-bright mode without magnification. Histopathologic diagnosis was used as the gold standard. Three hundred and twenty-four diminutive polyps in 164 patients were included for analysis (BLI: 162 polyps in 73 patients, NBI: 162 polyps in 91 patients). These polyps were located at colon proximal to sigmoid (61.1 and 58.0%) and rectosigmoid colon (38.9 and 42.0%) in the BLI and NBI groups, respectively. Most polyps (71.9%) were adenomatous with one malignant polyp (0.3%). BLI achieved 86.4% accuracy, 98.3% sensitivity, 55.6% specificity, 85.2% positive predictive value (PPV), and 92.6% negative predictive value (NPV), similar to NBI which exhibited 90.1% accuracy, 99.1% sensitivity, 67.4% specificity, 88.5% PPV, and 96.9% NPV in the diagnosis of adenomatous polyps. Based on the location of the polyp, both modes of IEE provided ≥ 95% NPV for diagnosis of adenomatous polyps at the rectosigmoid colon. BLI-bright and NBI modes of IEE have similar accuracy in differentiation between neoplastic and non-neoplastic lesions of diminutive polyps. Both modes provided ≥ 90% NPV which allows for the adaptation of the American Society of Gastrointestinal Endoscopy "diagnose-and-leave" recommended strategy for diminutive polyps at the rectosigmoid colon.

Effective Endoscopic Delineation With Acetic Acid Spray and Narrow Band Imaging in Underwater Endoscopic Mucosal Resection for Sessile Serrated Lesion.

Effective Endoscopic Delineation With Acetic Acid Spray and Narrow Band Imaging in Underwater Endoscopic Mucosal Resection for Sessile Serrated Lesion.

Texture and color enhancement imaging in magnifying endoscopic evaluation of colorectal adenomas.

BACKGROUNDOlympus Corporation has developed texture and color enhancement imaging (TXI) as a novel image-enhancing endoscopic technique.AIMTo investigate the effectiveness of TXI in identifying colorectal adenomas using magnifying observation.METHODSColorectal adenomas were observed by magnified endoscopy using white light imaging (WLI), TXI, narrow band imaging (NBI), and chromoendoscopy (CE). This study adopted mode 1 of TXI. Adenomas were confirmed by histological examination. TXI visibility was compared with the visibility of WLI, NBI, and CE for tumor margin, and vessel and surface patterns of the Japan NBI expert team (JNET) classification. Three expert endoscopists and three non-expert endoscopists evaluated the visibility scores, which were classified as 1, 2, 3, and 4.RESULTSSixty-one consecutive adenomas were evaluated. The visibility score for tumor margin of TXI (3.47 ± 0.79) was significantly higher than that of WLI (2.86 ± 1.02, P < 0.001), but lower than that of NBI (3.76 ± 0.52, P < 0.001), regardless of the endoscopist’s expertise. TXI (3.05 ± 0.79) had a higher visibility score for the vessel pattern of JNET classification than WLI (2.17 ± 0.90, P < 0.001) and CE (2.47 ± 0.87, P < 0.001), but lower visibility score than NBI (3.79 ± 0.47, P < 0.001), regardless of the experience of endoscopists. For the visibility score for the surface pattern of JNET classification, TXI (2.89 ± 0.85) was superior to WLI (1.95 ± 0.79, P < 0.01) and CE (2.75 ± 0.90, P = 0.002), but inferior to NBI (3.67 ± 0.55, P < 0.001).CONCLUSIONTXI provided higher visibility than WLI, lower than NBI, and comparable to or higher than CE in the magnified observation of colorectal adenomas.

A Comprehensive Monitoring and Assessment System for Multiple Fisheries Resources in the Northwest Pacific Based on Satellite Remote Sensing Technology

Monitoring the dynamics of ocean-going fishing vessels is essential for fisheries stock assessment and management. In this paper, coupled fishery and remote sensing methods were applied to propose a satellite-based fishery resource monitoring and assessment system (SFRMAS) that supported chub mackerel (Scomber japonicus), Pacific saury (Cololabis saira) and neon flying squid (Ommastrephes bartramii) fishing activities in the northwest Pacific. A multispecies fishing vessel database was generated in the SFRMAS using visible infrared imaging radiometer suite day/night band (VIIRS/DNB) images, automatic identification system (AIS) data and multimarine remote sensing resources. The results showed that the VIIRS/DNB vessel detection (VVD) and AIS vessel detection approaches improved the extraction accuracy of multispecies fishing vessels. A high productivity area with a higher chlorophyll a concentration had the most concentrated distribution of mackerel vessels. The widest longitudinal migrations of the neon flying squid and the latitudinal ranges of Pacific saury were highly related to sea surface temperature (SST), which was well captured by the movement of the fishing vessels. In the SFRMAS, the accuracy of detected multispecies fishing days and fishing catches was mostly between 60 and 90%, which can provide data support for fisheries stock assessment and management.

Obstructive sleep apnoea patients vs laryngopharyngeal reflux disease: Non-invasive evaluation with NBI and pepsin detection in tears.

Obstructive sleep apnea (OSA) and laryngopharyngeal reflux disease (LPR) are two common diseases that lower patients’ quality of life. OSA is defined by cyclic events of airflow obstruction that occurs during sleep, while LPR is characterized by upper airway inflammatory signs and symptoms due to the return of gastroduodenal gaseous and liquid elements. pH-metry is the gold standard in LPR diagnosis, but considering its invasiveness among other negative traits, questionnaires that catalog symptoms and signs of the disease such as reflux symptom index (RSI) and reflux finding score (RFS) are preferred. Moreover, LPR can be evaluated by testing the presence of pepsin in tears, and narrowband imaging (NBI) has been introduced for the early diagnosis of larynx oncological disease. This paper aims to test whether LPR is more frequent in OSA patients than in control ones, performing a non-invasive protocol composed of RSI and RFS test (with light vs. NBI techniques) followed by pepsin detection in tears. Sixty-eight LPR patients were enrolled in the study (45 with OSA and 23 without OSA). A strong linear relationship between apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) was found, and patients who presented pepsin in tears had higher values of AHI and ODI in comparison to patients without it. Pathological RFS and NBI showed higher values of AHI and ODI in comparison to the control group. Furthermore, pathological RSI showed higher values of AHI and ODI in comparison to the control group. In conclusion, this diagnostic combined non-invasive protocol may be a good method to perform an early diagnosis of LPR.

Effects of Mixture Mode on the Canopy Bidirectional Reflectance of Coniferous–Broadleaved Mixed Plantations

One of the main initiatives for China to achieve the goal of being carbon neutral before 2060 is transforming monocultures into mixed plantations in subtropical China, because mixed forests possess a higher quality than monocultures in various ways. Very high spatial resolution (VHR) satellite imagery is very promising to precisely monitor the transformation process under the premise of clarifying the canopy reflectance anisotropy of mixed plantations. However, it is almost impossible to understand the canopy reflectance anisotropy of mixed plantations with real satellite data due to the extreme lack of multiangular VHR satellite images. In this study, the effects of the mixture mode on the canopy bidirectional reflectance factor (BRF) were comprehensively analyzed with simulated VHR images. The three-dimensional (3D) Discrete Anisotropic Radiative Transfer model (DART) was used to construct a pure coniferous scene, a pure broadleaved scene, and 27 coniferous–broadleaved mixed plantation scenes containing 3 mixture patterns (i.e., mixed by single trees, mixed by stripes, and mixed by patches) and 9 mixing proportions (i.e., from 10% to 90% with the interval of 10%), and to simulate red (R) and near-infrared (NIR) VHR images for these 3D scenes at both the solar principal plane (SPP) and perpendicular plane (PP) under different solar-viewing geometries. Negative correlations were generally found between the canopy BRF and the ratio of conifers in a mixed stand. The anisotropy of conifer dominated plantations is more prominent than broadleaf dominated plantations, especially for the single tree mixture. Although the level of anisotropy is much lower for PP than SPP, it should not be ignored, especially for the R band. Observations under large viewing zenith angles at PP are more preferred to study the effect of mixing proportions, followed by forward observations at SPP. The R band image has higher potential to distinguish mixture patterns for broadleaf-dominated situations, while the NIR band image has a higher potential for conifer-dominated situations. Furthermore, the canopy BRF generally increases with the solar zenith angle, and one meter can be considered as the optimal spatial resolution for the optical monitoring of the mixture mode. The findings of the current study add some valuable theoretical knowledge for the accurate monitoring of coniferous–broadleaved mixed plantations with VHR imagery.

Dense channel splitting network for MR image super-resolution

High spatial resolution is desirable in magnetic resonance imaging (MRI) as it can provide detailed anatomical information, facilitating radiologists with accurate quantitative analysis. Super-resolution (SR) algorithms are effective approaches to enhance MR images' spatial resolution. In the past few years, convolutional neural network (CNN)-based SR methods have significantly improved and outperformed conventional ones. However, existing CNN-based SR methods usually do not explicitly consider the frequency property of images, leading to the limited representation of high-frequency components reflecting image details. To alleviate this problem, a dense channel splitting network (DCSN) algorithm is proposed to process the frequency bands for better feature detection. Specifically, a channel splitting module, a cascaded multi-branch dilation module, and a dense-in and recursive-out mechanism are designed to separate frequency bands of MR images and forward the high-frequency information to deeper layers for reconstruction. Several experiments are performed on real T2 brain and PD (proton density) knee images. The results indicate that the proposed network is superior to conventional CNN-based SR methods.

Achieving sustainable development goals through the study of urban heat island changes and its effective factors using spatio‐temporal techniques: The case study (Tehran city)

AbstractThe present study is aimed at investigating the spatio‐temporal structure of land surface temperature (LST) and its interrelationship with physical and biophysical land surface properties, such as land use, vegetation, the indicators of climate changes and air pollution, and the density of buildings in Tehran city of Iran, using Landsat 5, 7 and 8 thermal bands of satellite images in cold and hot seasons during the 1984–2020 period. The results indicate that the mean LST of Tehran city has gradually increased over time. LST results in a hot season, such as May showed that the changes in 1990 were between 6 and 44°C, in 2000, between 20 and 44°C, in 2013 between 14 and 42°C and in 2020 the changes were between 15 and 51°C. Also, the formation of hot spots in the west and south demonstrated the direct connection pattern of the distribution of space. According to the results, a strong negative LST‐NDVI and positive LST‐NDBI correlation are observed throughout the period. In addition, as to the other indices, the results indicate a positive correlation among LST, and air pollution indicators (NO2, SO2, CO, CO2, PM2.5, and PM10 ppm), a negative correlation with precipitation and a positive correlation with temperature in climatic indices and negative LST‐ Humidity, NDWI, and NDMI. These show the regions where water can weaken the UHI effect in the city. LST also has a positive correlation with buildings density and population centers, as well as industries concentrated in the western regions of Tehran that are subjected to rising LST. The mixed urban landscape of the city is closely related to the changes of the LST and the indicators, including NDVI, different land covers, and urban industrial and commercial activities. Finally, the regional vulnerability of Tehran as a metropolis was determined by using geographic information systems and spatio‐temporal methods. Based upon the results, the western regions of Tehran had more urban vulnerabilities in terms of thermal phenomena and the northern regions remain with the least vulnerability.