NIR Bands Research Articles

Broadband photodetectors with enhanced performance in UV–Vis–NIR band based on PbS quantum dots /ZnO film heterostructure

Broadband photodetectors with enhanced performance in UV–Vis–NIR band based on PbS quantum dots /ZnO film heterostructure

Modeling strategies and influencing factors in retrieving canopy equivalent water thickness of mangrove forest with Sentinel-2 image

Canopy equivalent water thickness (EWT) is an essential indicator of plant water status related to plant growth, temperature maintenance and transpiration. To our knowledge, this study was the first to map the mangrove canopy EWT using Sentinel-2 image at the reserve scale, aiming to explore three modeling strategies in retrieving mangrove canopy EWT, including machine learning models (Random Forest Regression (RFR) and Adaptive Boosting (Adaboost)), radiative transfer models (RTMs, PROSAIL-D and biophysical processor in Sentinel application platform) and hybrid models (PROSAIL-D + RFR and PROSAIL-D + Adaboost). We further investigated the impacts of four ecogeographical factors (species distribution, slope, elevation and distance to dam) on the spatial distribution of canopy EWT using Geodetector method. The results showed that Adaboost (R2 = 0.593, RMSE = 0.771 kg/m2 and RPIQ = 1.820), PROSAIL-D (R2 = 0.451, RMSE = 0.937 kg/m2 and RPIQ = 1.537) and PROSAIL-D + Adaboost (R2 = 0.501, RMSE = 0.890 kg/m2 and RPIQ = 0.887) was the optimal machine learning, RTM and hybrid model in retrieving mangrove canopy EWT, respectively. The red-edge3, NIR and SWIR bands of Sentinel-2 imagery were sensitive to canopy EWT. Moreover, Geodetector analysis demonstrated that species distribution had the most significant impact on canopy EWT, with the interaction between species and distance to dams contributing the most to the spatial difference of canopy EWT. In conclusion, this study suggests that Adaboost model and the hybrid method of PROSAIL-D and Adaboost were promising to map large-scale canopy water conditions in mangrove ecosystems using Sentinel-2 imagery.

Water transparency analysis in fish farming environment through unmanned aerial vehicles

Since water transparency in a fish farming environment is an important parameter in fish development, and it is important that the fish farmers be attentive to the control of the same. However, the process of measuring water transparency in fish ponds is usually done manually, which makes it expensive and inefficient. In order to solve this problem, a prototype was developed containing 2 optical sensors capable of capturing images in the NIR, R, G and B bands, connected to a microcomputer coupled to a Remotely Piloted Aircraft (RPAS), in order to capture aerial images of water and estimate water transparency through an image processing algorithm. The prototype was tested by an experiment conducted in a rural estate of the city of Toledo – PR, where 66 images were captured on a conical target, constructed to serve as a reflective reference for the images, comparing the results with the measurements collected by a Secchi disk, using Pearson's correlation analysis. The results showed a strong correlation (r = 0.98) in the estimates made by the NIR band processing, followed by the RGB, R, G and B bands. The developed system proved to be effective in data collection, processing and analysis, optimizing the fish farming management.

SMALL WETLANDS IN THE BRAZILIAN SAVANNA(CERRADO): AN UNDERESTIMATED FRESHWATER RESOURCE

In Brazil, in scientific research and public policy have largely ignored small wetlands, they are not specifically covered by current environmental laws and reference standards. Furthermore, the lack of information about, small wetlands has made their protection and management difficult. Large-scale land use changes that modify the hydrology of low-order streams often lead to the accelerated degradation and loss of small wetlands. The aim of this study conducted in Brazilian Cerrado Biome area, was to map the type, size, and distribution of small wetlands in ancatchment area). Areas represented in 12 high-resolution multispectral images were individually classified in a geographic object-based image analysis (GEOBIA), resulting in a sensitivity or recall 0.872 (~87%). The 7,132 mapped wetlands had a total area of 163km2, corresponding to a density of 4.27wetlands km−2, with smallest wetlands smaller than 4.0 hectares (90.5%). The following descriptors were applied to map small wetlands: feature: asymmetry, rectangular fit, mean nir band, compactness, density, and length. Seven types of natural wetlands and four types of man-made wetlands were accordingly identified in the study area. Their classification within a system that follows the hierarchy of the Brazilian wetland classification system is proposed. Recommendations for the future study and protection of wetlands was are also provided.

Comparison of multi-source satellite remote sensing observations for monitoring the variations of small lakes: a case study of Dai Lai Lake (Vietnam)

Abstract This study compares the capability of Sentinel-1, Sentinel-2, and PlanetScope (PS) satellites in monitoring the variations of surface water of Dai Lai Lake, located in North Vietnam, for the 2018–2023 period. The analysis involves the utilization of Google Earth Engine to partially process Sentinel-1 and Sentinel-2 observations, while PS observations are processed using local computers, to generate VH-polarized backscatter coefficient, Normalized Difference Water Index (NDWI), and Modified of Normalized Difference Water Index (MNDWI) maps. The method for making binary water/non-water maps primarily employs the Otsu algorithm on each single map derived from the previous step. Findings reveal that the lake's water extent remains relatively stable over the 6-year period, and is not strongly affected by the seasonal cycle. Although the spatial distribution patterns of the lake exhibit significant similarity, average water extent of the lake derived from 3-m resolution PS imagery is about 2.17 and 5.60% more than that obtained from 10-m resolution Sentinel-2 and Sentinel-1 imagery, respectively. PS observations are effective for monitoring small lakes, but it is advised to check the quality of its NIR band. Sentinel-2 observations prove great effectiveness for lake monitoring, using both NDWI and MNDWI. For Sentinel-1 observations, potential misclassifications could arise due to similarities in VH-polarized backscatter coefficients between water surfaces and other flat surfaces.

Картографирование лесных насаждений Волгоградской области по данным ДЗЗ с использованием индексов BSFI и NDWI

The possibilities of adjusting the results of mapping wood plantations based on the bi-seasonal forest index (BSFI) according to Sentinel-2 data and calculated seasonal composite images of the NDWI index maximum values are presented. It was found out that with using a seasonal composite image of the mentioned values, calculated with the NIR and SWIR2 ranges, the manufacturer`s accuracy in raster cross-validation increases from 82,5 to 92,8 %. At the same time, the initial areas of wood zones decreased by 7 %, and those of false identifications shrank by 67 %. Comparison of mapping results with statistical data showed small differences in areas with a large number of natural forest plantations (in 18 of 33 areas they do not exceed 15 % of the design ones). Moreover, in lands with a large number of protective green spaces, discrepancies can reach up to 210 % of the design values. It is shown that significant distinctions in design and actual mapping materials can be associated with poor preservation, fires, illegal logging, as well as problems of accounting protective forest plantings. A fairly accurate method for adjusting BSFI was proposed (the manufacturer’s accuracy increased from 82,5 to 92,8 %) based on the maximum composite NDWI image calculated using the NIR and SWIR2 bands of the Sentilel-2A and Sentilel-2B satellites in a test area within the Volgograd region

Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7–10.8 %, 3.7–7.5 %, and 1.6–14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

Mapping surface water dynamics (1985–2021) in the Hudson Bay Lowlands, Canada using sub-pixel Landsat analysis

The fate of the carbon stored in frozen peatlands in Canada's Hudson Bay Lowlands (HBL) depends strongly on water, with wetter conditions favoring long-term storage. Dynamic surface water products generated from historical Landsat data exist to inform surface water trends in the HBL based on binary classifications of land vs water at 30 m spatial resolution. However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to fully capture their dynamics. In this paper, we leverage an existing binary dynamic surface water product to generate a spatially and temporally comprehensive Landsat sub-pixel surface water time-series over the HBL. We first generate Landsat Red, NIR and SWIR composites from 1985 to 2021 and evaluate data-driven machine learning and physical models to derive sub-30 m water fractions across the entire mapping domain. Data-driven models were calibrated on 30 m water fractions obtained from 16 of 17 WorldView scene water fraction maps and assessed on the 17th held-out scene in a jackknife validation. Data-driven models were also calibrated by relating scaled Landsat reflectance to water fractions obtained from binary water masks at corresponding scales from 90 to 270 m. Physical models involved simple linear unmixing using a new approach that samples land endmembers in the vicinity of each pixel to represent land reflectance, and a global water endmember sampled beneath permanent water in each image. The assumption of linear scaling was verified at sub-30 m to 270 m resolution for NIR, SWIR and NDVI, which were all highly correlated with water fraction and had no significant differences among water fraction – spectral feature relationships across the full range of scales. Each of 11 different methods and input spectral features were evaluated against WorldView water fractions and ranked based on nine criteria. We found that machine learning using all spectral features including local NIR land endmembers performed best among data-driven approaches, and linear unmixing of the NIR band using local land endmembers performed best among physical. Both best-performing methods were applied to the entire time-series and surface water area trends were calculated and compared with trends obtained from binary water masks. All three methods agree on the direction of surface water change towards wetting, with sub-pixel inter-annual variation agreeing with stream discharge noted in other studies.

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

Intramolecular Electron Transfer in Multi-Redox Systems Based on Cyclic [3]Spirobifluorenylene Compound.

Cyclic [3]spirobifluorenylene with bulky alkyl groups at the ends (1) was designed and synthesized to investigate the electron transfer phenomena in a π-conjugated system including orthogonal π-conjugated chains. The three bifluorenyl units in 1 are conjugated to each other via spiro-conjugation, resulting in the splitting of the HOMO levels to a small extent. Therefore, the SOMO-HOMO gap of the radical cation species is small, which is considered to allow the facile intramolecular electron transfer. In the TD-DFT calculations for the radical cation species of 1, the geometry-featured interchain electronic transitions were visualized by NTO calculations. The radical cation species of 1 generated by chemical oxidation with SbCl5 exhibited a broadened and lower-energy shifted NIR absorption band compared to those of its partial structures. Considering the results of the TD-DFT calculations, the NIR band of the radical cation of 1 was attributed to the intramolecular electron transfer processes among the bifluorenyl units in the macrocycle. ESR experiments also indicated the delocalization of a spin of 1·+ in the whole molecule via hole hopping in the ESR time scale at room temperature. This work demonstrates the usefulness of spiro-conjugation as a bridging unit in molecular wires to facilitate smooth electron transfer.

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R2 of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time.

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-m resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and improve the generalization capability of the model. We tested the model on hold out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We noticed NIR band at the early and late stage of the apple harvest season (main crop in the region) to be of critical importance for the model. Finally, we used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps, and made the code open-source.

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

Design of the dual-band shared-aperture asynchronous zoom optical system using focus tunable lenses.

To meet the increasing demand of the current market and the diversity of application scenarios, combine the zoom system and the multi-band shared-aperture system, and fully harness their respective advantages, this paper proposes a dual-band shared-aperture asynchronous zoom optical system using focus tunable lenses (FTLs). To address the lack of available patents for such systems, we designed a sub-system simultaneous iterative optimization algorithm to calculate the initial structure parameters. This synchronous iterative optimization approach can strengthen the connection between sub-systems and compensate for the shortcomings of current mainstream design methods. The initial structure constructed in this way has a good performance in terms of structural stability and optimization potential. Based on these methods, we successfully designed an optical system that can work in both VIS and NIR bands, and the two sub-systems can zoom independently. The design results possess good performance in terms of distortion control, aberration correction, and volume control.

High NA and polarization-insensitive ultra-broadband achromatic metalens from 500 to 1050 nm for multicolor two-photon endomicroscopy imaging.

Multicolor two-photon endomicroscopy has become a highly competitive tool for functional imaging in biomedical researches. However, to make the imaging system miniature and applicable for freely behaving animal brain activity, metalenses have received much attention in compact imaging systems. For high resolution multicolor imaging and maximizing fluorescence collection, there is a challenge metalenses faced to achieve large numerical aperture (NA) and focus the NIR excitation and VIS emission lights of multiple fluorophores to the same distance simultaneously because of the limitation of the group delay range of the meta-units. In this paper, we proposed a high NA and polarization-insensitive ultra-broadband achromatic metalens specifically for achromatically focusing the excitation and emission light of multiple fluorophores commonly used in neuroscience studies. TiO2 and Si meta-unit libraries composed of heights, widths and the corresponding phase and group delay were constructed, and the optimal meta-units were selected by particle swarm optimization algorithm to engineer the dispersion of metalens in the VIS band and NIR band, respectively. Combining dispersion engineering with spatial multiplexing, the proposed metalens achieved the maximal effective NA up to 0.8 and large achromatic bandwidth ranging from 500 nm to 1050 nm, which exhibited the coefficient of variation of focal lengths was only 3.41%. The proposed achromatic metalens could successfully achromatically focus different fluorescence with any polarization, which was suitable for most fluorophores. Our results firmly establish that the proposed metalens can open the door to high resolution and minimally invasive multicolor two-photon functional imaging in intravital deep brain.

Electronic Structure, Spectroscopy, and Redox Properties of Highly Soluble (μ-oxo)Bis[Tetra(tert-butyl)(phthalocyaninato)Iron(III)] Complex and Its Pyridine Adduct Under Anaerobic Conditions in Non-Coordinating Solvents

The solid-state Mössbauer spectra of a highly soluble (μ-oxo)bis[tetra(tert-butyl)(phthalocyaninato)iron(III)] complex 1 ((PctBuFe)2O) consist of two doublets that represent bent geometry μ-oxo(1) (1a, ΔEQ = 0.43 mm/s, T = 10K) and linear geometry μ-oxo(2) (1b) isomers (ΔEQ = 1.40 mm/s, T = 10K) with the ratio between two isomers depending on the purification method. Both isomers were found to be diamagnetic and transform entirely in 1a isomer in solution. The room- and low-temperature MCD spectra of 1a show one Faraday A- and one B-terms between 670 and 720 nm, which correlate with the 690 nm band and 709 nm shoulder observed in the UV-Vis spectrum of this compound. UV-Vis and MCD spectra of 1a are independent of the temperature. Both 1a and 1b are diamagnetic between room temperature and 4 K. Electrochemical experiments show up to three oxidation and up to four reduction processes in 1a. Its oxidation under spectroelectrochemical or chemical (in the absence of oxygen-containing oxidants) oxidation conditions in non-coordinating solvents results in the formation of a broad NIR band around 1195 nm (first oxidation) and 1264 nm (second oxidation). The MCD spectra of the redox-active species show a Faraday B-term signal with negative amplitude in this region and are very different from those in monomeric PctBu(1-)FeIIIX2 complexes 5X (X = Cl- or CF3CO2 -). Pyridine adduct of 1a ((PyPctBuFe)2O; 2Py) is paramagnetic (μB = 2.19, g = 2.11, and J = -6.1cm-1) and has a major peak at 627 nm in UV-Vis spectrum, which is associated with MCD pseudo A-term. Density Functional Theory (DFT) and Time-Dependent DFT (TDDFT) calculations, along with Kasha’s and Spano’s exciton coupling theories, were used to explain unusually red-shifted intense transitions in 1a as well as H-aggregate like spectra of the pyridine adduct 2Py.

Multispectral Food Classification and Caloric Estimation Using Convolutional Neural Networks.

Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the use of multi-spectral images. A method of fusing the RGB image and the images captured at ultra violet, visible, and near-infrared regions at center wavelengths of 385, 405, 430, 470, 490, 510, 560, 590, 625, 645, 660, 810, 850, 870, 890, 910, 950, 970, and 1020 nm was adopted to improve the accuracy. A convolutional neural network (CNN) was adopted to classify food items and estimate the caloric amounts. The CNN was trained using 10,909 images acquired from 101 types. The objective functions including classification accuracy and mean absolute percentage error (MAPE) were investigated according to wavelength numbers. The optimal combinations of wavelengths (including/excluding the RGB image) were determined by using a piecewise selection method. Validation tests were carried out on 3636 images of the food types that were used in training the CNN. As a result of the experiments, the accuracy of food classification was increased from 88.9 to 97.1% and MAPEs were decreased from 41.97 to 18.97 even when one kind of NIR image was added to the RGB image. The highest accuracy for food type classification was 99.81% when using 19 images and the lowest MAPE for caloric content was 10.56 when using 14 images. These results demonstrated that the use of the images captured at various wavelengths in the UV and NIR bands was very helpful for improving the accuracy of food classification and caloric estimation.

Multi-Attention Multi-Image Super-Resolution Transformer (MAST) for Remote Sensing

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.

Tunable band-selective photodetector based on sputter-deposited SnOx thin-films: Effect of reactive gas pulsing process

In this work, high-performance SnOx band-selective photodetectors (PDs) were realized by DC magnetron sputtering technique. The reactive gas pulsing process (RGPP) was implemented with pulsing period fixed at P = 20 s to adjust tin and oxygen concentrations in the film. The impact of pulsed oxygen time on the structural, morphological and photosensing properties of SnOx-based PDs was investigated. The fabricated SnOx-based PD at a high duty cycle of 80% of P demonstrated high UV photodetection capabilities and solar-blind characteristic with a high responsivity of 21.8 mA/W, a high signal to noise ratio of 6 × 104 and a specific detectivity of about 5 × 1011 Jones. It was also revealed that the use of a short oxygen pulsing time 8 s paves the way for the realization of multispectral SnOx PD, demonstrating a high responsivity values of 36.45 mA/W, 36.4 mA/W and 34 mA/W over UV, Visible and NIR bands, respectively. This is attributed to the role of using RGPP in modulating the film optoelectronic properties from metallic to insulator when the duty cycle and thus oxygen concentration in the films changed from pure tin to overstoichiometric SnO2 compound. The prepared SnOx PDs demonstrated many advantageous features like low-noise, cost effective and high sensitivity. The obtained results proved that SnOx can be used as an alternative material for developing high-performance band-selective sensing devices.

Automatic labelling for semantic segmentation of VHR satellite images: Application of airborne laser scanner data and object-based image analysis

The application of deep learning methods to remote sensing data has produced good results in recent studies. A promising application area is automatic land cover classification (semantic segmentation) from very high-resolution satellite imagery. However, the deep learning methods require large, labelled training datasets that are suitable for the study area. Map data can be used as training data, but it is often insufficiently detailed for very high-resolution satellite imagery. National airborne laser scanner (lidar) datasets provide additional details and are available in many countries. Successful land cover classifications from lidar datasets have been reached, e.g., by object-based image analysis. In the present study, we investigated the feasibility of using airborne laser scanner data and object-based image analysis to automatically generate labelled training data for a deep neural network -based land cover classification of a VHR satellite image. Input data for the object-based classification included digital surface models, intensity and pulse information derived from the lidar data. The resulting land cover classification was then utilized as training data for deep learning. A state-of-the-art deep learning architecture, UnetFormer, was trained and applied to the land cover classification of a WorldView-3 stereo dataset. For the semantic segmentation, three different input data composites were produced using the red, green, blue, NIR and digital surface model bands derived from the satellite data. The quality of the generated training data and the semantic segmentation results was estimated using an independent test set of ground truth points. The results show that final satellite image classification accuracy (94–96%) close to the training data accuracy (97%) was obtained. It was also demonstrated that the resulting maps could be used for land cover change detection.