High Spectral Resolution Images Research Articles

Fusion of UNet and ResNet decisions for change detection using low and high spectral resolution images

Fusion of UNet and ResNet decisions for change detection using low and high spectral resolution images

Imaging Mass Spectrometry of Isotopically Resolved Intact Proteins on a Trapped Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometer.

In this work, we demonstrate rapid, high spatial, and high spectral resolution imaging of intact proteins by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on a hybrid quadrupole-reflectron time-of-flight (qTOF) mass spectrometer equipped with trapped ion mobility spectrometry (TIMS). Historically, untargeted MALDI IMS of proteins has been performed on TOF mass spectrometers. While advances in TOF instrumentation have enabled rapid, high spatial resolution IMS of intact proteins, TOF mass spectrometers generate relatively low-resolution mass spectra with limited mass accuracy. Conversely, the implementation of MALDI sources on high-resolving power Fourier transform (FT) mass spectrometers has allowed IMS experiments to be conducted with high spectral resolution with the caveat of increasingly long data acquisition times. As illustrated here, qTOF mass spectrometers enable protein imaging with the combined advantages of TOF and FT mass spectrometers. Protein isotope distributions were resolved for both a protein standard mixture and proteins detected from a whole-body mouse pup tissue section. Rapid (∼10 pixels/s) 10 μm lateral spatial resolution IMS was performed on a rat brain tissue section while maintaining isotopic spectral resolution. Lastly, proof-of-concept MALDI-TIMS data was acquired from a protein mixture to demonstrate the ability to differentiate charge states by ion mobility. These experiments highlight the advantages of qTOF and timsTOF platforms for resolving and interpreting complex protein spectra generated from tissue by IMS.

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.

Machine learning-based spectral and spatial analysis of hyper- and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by Ophiostoma novo-ulmi,) and American elm (Ulmus americana) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with O. novo-ulmi under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Substrate microtopographies induce cellular alignment and affect nuclear force transduction

Cellular physiology has been mainly studied by using two-dimensional cell culture substrates which lack in vivo-mimicking extracellular environment and interactions. Thus, there is a growing need for more complex model systems in life sciences. Micro-engineered scaffolds have been proven to be a promising tool in understanding the role of physical cues in the co-regulation of cellular functions. These tools allow, for example, probing cell morphology and migration in response to changes in chemo-physical properties of their microenvironment. In order to understand how microtopographical features, what cells encounter in vivo, affect cytoskeletal organization and nuclear mechanics, we used direct laser writing via two-photon polymerization (TPP) to fabricate substrates which contain different surface microtopographies. By combining with advanced high-resolution spectral imaging, we describe how the constructed grid and vertical line microtopographies influence cellular alignment, nuclear morphology and mechanics. Specifically, we found that growing cells on grids larger than 10 × 20 μm2 and on vertical lines increased 3D actin cytoskeleton orientation along the walls of microtopographies and abolished basal actin stress fibers. In concert, the nuclei of these cells were also more aligned, elongated, deformed and less flattened, indicating changes in nuclear force transduction. Importantly, by using fluorescence lifetime imaging microscopy for measuring Förster resonance energy transfer for a genetically encoded nesprin-2 molecular tension sensor, we show that growing cells on these microtopographic substrates induce lower mechanical tension at the nuclear envelope. To conclude, here used substrate microtopographies modulated the cellular mechanics, and affected actin organization and nuclear force transduction.

Wavelength-multiplexing high-spectral resolution imaging based on Fourier ptychographic microscopy

A wide field of view (FOV), high spectral resolution, and high spatial resolution are the targets of optical microscopic imaging. Fourier ptychographic microscopy (FPM) imaging can solve the contradiction between a wide FOV and spatial resolution. However, the spectral resolution of the samples is not accessible to typical LED-based FPM. Here, we report a wavelength-multiplexing high-spectral resolution reconstruction strategy based on the FPM of the halogen lamp. In contrast to the typical FPM setting, we can obtain spectral information of arbitrary wavelengths and greatly improve the sampling efficiency. We optimize and combine the wavelength-multiplexed decoupled algorithm with the high-spectral resolution reconstruction algorithm. We show that, by using our strategy, we can better reconstruct the image with a spectral resolution of 5 nm and maintain wide FOV and high spatial resolution. We believe the reported strategy may provide ideas for FPM-based spectral microimaging.

A image fusion and U-Net approach to improving crop planting structure multi-category classification in irrigated area

Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees).

Broadband High-Resolution Stigmatic Spectral Imaging in the XUV Range

Broadband High-Resolution Stigmatic Spectral Imaging in the XUV Range

Using Sentinel 2A spectral-zone images for qualitative assessment of soils in the Northern Forest-Steppe of Ukraine

The research described in this paper is aimed at developing a methodology for remote determination of arable soil quality in the form of a bonita score using high-resolution spectral images. To achieve this goal, the dependencies between the values of the soil bonitet score and the spectral characteristics of satellite images of land cover areas were established. The geospatial models of soil cover scoring were verified, the results of which show a fairly high degree of correlation between the results of traditional soil scoring and remote sensing (NDSI, r = 0.91), the error value of the soil cover score for individual agricultural production groups of soils ranges from 1 to 12 points. It is found that despite the traditionally established use of vegetation indices to determine the state of crops, their use to determine the level of soil fertility is no less informative. It has been shown that the paired use of NDVI and NDSI values allows for the construction of relevant equations for remote determination of soil fertility, where the first of them acts as an auxiliary (clarifying) index, and the second as an effective working index.

Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study

High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques.

A notch-mask and dual-prism system for snapshot spectral imaging

In this paper, we present a notch-mask and dual-prism based system for snapshot spectral imaging. Different from traditional spectrometers that utilize band-pass filters or narrow slits to prevent spectral overlap, losing most of the light emitted from the scene, the main idea of our system is to design a spatial notch mask that only occludes the light of sparsely distributed spectral samplings, and introduce a dual-prism architecture to form a non-dispersive RGB measurement on the detector, resulting in the system’s superiority of high light efficiency and high spatial resolution within the snapshot. Then, we develop a model-based algorithm to reconstruct a high-resolution spectral image from the captured RGB measurement, which is of high tolerance to calibration noise since the spectral information can be directly calculated by a minus operation. Simulated experiments have been conducted to verify the effectiveness of the proposed method, and we also build up a prototype system to further demonstrate the practical application and feasibility of our scheme.

Multi-Sensor Image Feature Fusion via Subspace-Based Approach Using $\ell _{1}$-Gradient Regularization

Image fusion is a technique of combining two or more images into a single image which is more informative from an interpretation point of view. With the rapid development of different synthetic aperture radar sensing satellites capturing information from the earth by measuring energy in different portions of the electromagnetic spectrum (narrow/wide-band), complementary information about the area captured by different satellites is available (e.g. high-resolution spectral and RGB images). However, the estimation of the full-resolution image may not be necessary for inference approaches, including the pixel-based classification. Instead, it is desirable to extract the relevant information embedded in the available data to improve the inference capabilities. This work proposes a computational framework to estimate features with high-spatial-resolution and appropriate spectral content by combining information from a multi-sensor system. The considered multi-sensor setup is a hyperspectral imaging system with a complementary RGB sensor. The proposed framework first extracts spatial features from the RGB image using morphological profiles. Then, the fusion model assumes that the extracted features, and the hyperspectral measurements, lie in different subspaces matrices. In addition, this work developed a joint optimization scheme to solve the feature fusion problem by integrating the alternating direction method of multipliers with the block coordinate descent method. The alternating optimization method estimates the spatial features in the fusion model by penalizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm of the spatial gradient magnitudes. The quality of extracted features is measured in terms of supervised pixel-based classification methods. Extensive simulations show that the proposed approach outperforms other state-of-the-art methods in terms of classification accuracy.

Photoelectric hybrid neural network based on ZnO nematic liquid crystal microlens array for hyperspectral imaging.

The miniaturized imaging spectrometers face bottlenecks in reconstructing the high-resolution spectral image. In this study, we have proposed an optoelectronic hybrid neural network based on zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA). This architecture optimizes the parameters of the neural network by constructing the TV-L1-L2 objective function and using mean square error as a loss function, giving full play to the advantages of ZnO LC MLA. It adopts the ZnO LC-MLA as optical convolution to reduce the volume of the network. Experimental results show that the proposed architecture has reconstructed a 1536 × 1536 pixels resolution enhancement hyperspectral image in the wavelength range of [400 nm, 700 nm] in a relatively short time, and the spectral accuracy of reconstruction has reached just 1 nm.

Underwater Hyperspectral Imaging System with Liquid Lenses

The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.

Spectral–Temporal Fusion of Satellite Images via an End-to-End Two-Stream Attention With an Effective Reconstruction Network

Due to technical and budget constraints on current optical satellites, the acquisition of satellite images with the best resolutions is not practicable. In this article, aiming to produce products with high spectral (HS) and temporal resolutions, we introduced a two-stream spectral–temporal fusion technique based on attention mechanism called STA-Net. STA-Net aims to combine high spectral and low temporal (HSLT) resolution images with low spectral and high temporal (LSHT) resolution images to generate products with the best characteristics. The proposed technique involves two stages. In the first one, two fused images are generated by a two-stream architecture based on residual attention blocks. The temporal difference estimator stream estimates the temporal difference between HS images at desired and neighboring dates. The reflectance difference estimator is the second stream. It predicts the reflectance difference between the input images (HS–LS) to map LS images into HS products. In the second stage, a reconstruction network combines the latter two-stream outputs via an effective learnable weighted-sum strategy. The two-stage model is trained in an end-to-end fashion using an effective loss function to ensure the best fusion quality. To the best of our knowledge, this work represents the first attempt to address the spectral–temporal fusion using an end-to-end deep neural network model. Experimental results conducted on two actual datasets of Sentinel-2 (HSLT:10 spectral bands and long revisit period) and Planetscope (LSHT: four spectral bands and daily images) images, which proved the effectiveness of the proposed technique with respect to baseline technique.

Research Status and Application Prospects of Astrophotonics

Astronomy is an observational discipline, and its improvement is driven by the progress of observation technology and instruments. The advancement of astronomy also constantly puts forward new requirements for observation instruments. Since the development of astronomy, the requirements for observing instruments have gradually become extreme, which brings great challenges in both cost and difficulty. In order to tackle the challenges, a future generation of astronomical optical technology and observation instruments based on new principles and technologies has become an inherent need to promote the advancement of astronomy. In recent years, the growth of integrated photonics has presented revolutionary opportunities for that of astronomical optical technology. On the basis, astrophotonics, an emerging interdisciplinary subject, can provide a new generation of high-performance optical terminal instruments with low cost and high integration (chip-based) for astronomical observation. Such instruments will play a vital role in space astronomical observation, large-scale spectral survey, high-resolution and high-precision spectral imaging, and other applications. This paper mainly introduces the main research contents and status quo of astronomical photonics starting from the instruments/device functions, briefly discusses the major problems in its development, and eventually forecasts its development prospect.

Юбилей «Геоботанического картографирования»: некоторые итоги

The journal “Geobotanical mapping” presents the main stages and directions of the vegetation cartography development in Russia (USSR) and abroad over the past 60 years. The 1960–1980s were a period of some theoretical generalizations, the development of new types of maps initiated by the V. B. Sochava’s botanical cartographic school in Irkutsk. Since the late 1980s up to the present thematic mapping has been actively improved due to implementation of GIS technologies, automatic interpretation of remote sensing data, and usage of high-resolution space spectral images. The main directions of the vegetation mapping published in the “Geobotanical mapping” journal at the beginning of XXI century are: 1) global and regional differentiation of vegetation cover, macrostructures, evolutionary constructions; 2) vegetation dynamics, predictive mapping; 3) ecological and phytoecological mapping, ecoregions; 4) large-scale inventory and assessment mapping; 5) spatial structure of vegetation cover, typology of territorial units; 6) mapping as the basis for geobotanical zoning; 7) mapping of habitats and biotopes. The vegetation map organizes the information that is needed for mapping — data on vegetation classification; ecology of vegetation, the distribution of plant communities, their types, dominant’s coverage, vertical and horizontal structure of communities. The map is suitable for further cartometric and morphometric analysis and obtaining map information that was not explicitly included in the mapping process. The cartometric indicators such as size, shape, length of the perimeter of contours, areal ratio of various types etc. require their carefully tested interpretation from the standpoint of knowledge of processes and phenomena in vegetation. The vegetation map is not only the final result of vegetation study, it is a reason to organize scientific branches of geobotany — vegetation classification, dynamics, structure — into a certain sequence. Geobotanical mapping raises the question of correspondence of two basic concepts of vegetation science — the plant community and the vegetation cover continuum. The study of characteristics of vegetation cover structures, geobotanical zoning units helps us to the study of vegetation continuum, and the categories of common species, differentiating species and some others can be implemented.

Analytical design of a cemented-curved-prism based integral field spectrometer (CIFS) with high numerical aperture and high resolution.

Snapshot hyperspectral imaging is superior to scanning spectrometers due to its advantage in dimensionality, allowing longer pixel dwell time and higher data cube acquisition efficiency. Due to the trade-off between spatial and spectral resolution in snapshot spectral imaging technologies, further improvements in the performance of snapshot imaging spectrometers are limited. Therefore, we propose a cemented-curved-prism-based integral field spectrometer (CIFS), which achieves high spatial and high spectral resolution imaging with a high numerical aperture. It consists of a hemispherical lens, a cemented-curved-prism and a concave spherical mirror. The design idea of aplanatic imaging and sharing-optical-path lays the foundation for CIFS to exhibit high-resolution imaging in a compact structure. The numerical model between the parameters of optical elements and the spectral resolution of the system is established, and we analyze the system resolution influenced by the hemispherical lens and the cemented-curved-prism. Thus, the refractive index requirements of the hemispherical lens and the cemented-curved-prism for the optimal spatial and spectral resolution imaging of the system are obtained, providing guidance for the construction of CIFS. The designed CIFS achieves pupil matching with a 1.8 f-number lenslet array, sampling 268 × 76 spatial points with 403 spectral channels in the wavelength band of 400 to 760 nm. The spectral and spatial resolution are further evaluated through a simulation experiment of spectral imaging based on Zemax. It paves the way for developing integral field spectrometers exhibiting high spatial and high spectral resolution imaging with high numerical aperture.

Ultrafast Z-spectroscopic imaging in vivo at 3T using through-slice spectral encoding (TS-UFZ).

Acquisition of high-resolution Z-spectra for CEST or magnetization transfer contrast (MTC) MRI requires excessive scan times. Ultrafast Z-spectroscopy (UFZ) has been proposed to address this; however, the quality of in vivo UFZ spectra has been insufficient. Here, we present a simple approach to improve this. UFZ imaging acquires full Z-spectra by encoding the spectral dimension spatially via a gradient applied concurrently with the RF saturation pulse. Different from previous implementations, both this saturation gradient and its readout were applied in the slice direction, resulting in a relatively uniform voxel composition. Phase-encoding was applied in both in-plane directions, allowing additional under-sampling and acceleration. In phantoms, UFZ imaging with through-slice Z-spectral encoding (TS-UFZ) provided Z-spectra of salicylic acid and egg white in excellent agreement with conventional acquisitions. In vivo brain Z-spectra were influenced by flow through the imaging slice which affected the Z-spectral baseline. Still, CEST signals could be quantified after baseline fitting and mapping the residual CEST signal. Amide proton transfer (APT) contrast intensities obtained by TS-UFZ were on the same order of magnitude as conventional CEST but with different contrast across slice which likely is a result of different tissue regions contributing. TS-UFZ approach improves signal stability and spectral uniformity over previous implementations and allows high spectral-resolution imaging of saturation transfer effects in the human brain at 3T. This implementation allows for further acceleration by reducing phase encoding steps and thus opens up the possibility of mapping dynamic CEST signals in vivo with a practical temporal resolution.

Detecting pine wilt disease at the pixel level from high spatial and spectral resolution UAV-borne imagery in complex forest landscapes using deep one-class classification

Pine wilt disease (PWD) poses a serious threat to the worldwide pine forest resources. Unmanned aerial vehicle (UAV) remote sensing has been widely used for PWD control, due to its flexibility and efficiency. Although pixel-level detection can obtain fine detection boundaries, there have been few related works in complex scenes because of the difficulty of setting a preset category system and the poor generalization. A preset category system establishes which categories are to be labeled, and is necessary for traditional pixel-level detection. However, the poor generalization leads to an obvious accuracy drop when detecting PWD in new scenes. In the proposed approach, to address the first issue, one-class classification (OCC) is introduced to detect diseased pixels, focusing only on the category of diseased pine trees. However, the numerous objects but low PWD pixel proportion makes the model optimization unbalanced, for which balanced unbiased detection risk estimation is proposed. To address the second issue, a novel model consisting of three-dimensional (3D) convolutional layers and transformer blocks is proposed to extract more robust features. A novel PWD detection framework based on deep OCC is finally proposed to achieve fine pixel-level PWD detection results. PWD detection experiments were conducted on eight UAV H2 (high spatial and spectral resolution) image strips. In total, 300 PWD samples from strip 1 (accounting for roughly 0.009 % of the total pixels) and 400 unlabeled pixels formed the training set. The test experiments were conducted in the remaining seven strips to validate the model generalization. Satisfactory quantitative results (F1-score greater than 0.9) were obtained for all the test strips. The results indicate that the proposed method has a powerful ability to detect PWD in pine trees, even when the PWD proportion is low, and shows better model generalization than the traditional pixel-level detection methods.