Narrow Spectral Bands Research Articles

FCA-Net: A Fast Inference and Channel Attention Based Network for Hyperspectral Image Classification

Hyperspectral imaging (HSI) is a competitive remote sensing technique used in various fields such as land cover mapping and environmental monitoring. Each hyperspectral imaging (HSI) scene is comprised of numerous narrow and contiguous spectral bands, rendering the extraction of information from HSI data cubes a challenging and computationally intricate endeavor. Convolutional neural networks (CNNs) have garnered widespread adoption for HSI classification due to their impressive performance. Nevertheless, the substantial number of internal parameters within CNNs engenders high computational and memory requirements, resulting in inefficient floating-point operations per second (FLOPS), particularly when faced with frequent memory access and an abundance of operators. To address this issue, this paper proposes a novel framework named Fast Inference and Channel Attention-based Network (FCA-Net). Specifically, the framework introduces a lightweight convolutional layer and a channel attention mechanism (CAM) to enhance the extraction of spatial and spectral information within the network. The proposed FCA-Net significantly reduces computational costs while maintaining reliable classification results and can perform fast processing on GPU or even CPU, making it a promising option for embedded systems. Furthermore, the optimized global computational cost, including reduced demand for compute power and memory, results in lower energy consumption, which has previously been proven advantageous for improving deep model performance.

Micro 4D Imaging Sensor Using Snapshot Narrowband Imaging Method.

The spectral and depth (SAD) imaging method plays an important role in the field of computer vision. However, accurate depth estimation and spectral image capture from a single image without increasing the volume of the imaging sensor is still an unresolved problem. Our research finds that a snapshot narrow band imaging (SNBI) method can discern wavelength-dependent spectral aberration and simultaneously capture spectral-aberration defocused images for quantitative depth estimation. First, a micro 4D imaging (M4DI) sensor is proposed by integrating a mono-chromatic imaging sensor with a miniaturized narrow-band microarrayed spectral filter mosaic. The appearance and volume of the M4DI sensor are the same as the integrated mono-chromatic imaging sensor. A simple remapping algorithm was developed to separate the raw image into four narrow spectral band images. Then, a depth estimation algorithm is developed to generate 3D data with a dense depth map at every exposure of the M4DI sensor. Compared with existing SAD imaging method, the M4DI sensor has the advantages of simple implementation, low computational burden, and low cost. A proof-of-principle M4DI sensor was applied to sense the depth of objects and to track a tiny targets trajectory. The relative error in the three-dimensional positioning is less than 7% for objects within 1.1 to 2.8 m.

High-Precision Microdisk Laser Particles by Active Photoelectrochemical Etching for Cell Barcoding

To study the spatio-temporal evolution of individual cells in an intact tissue or other specific microenvironment, organic fluorophores are widely used. Owing to the broad emission linewidth (full width at half maximum exceeding 50 nm) of the organic fluorophore molecules, only limited number of fluorophores with non-overlapping spectral signatures can be simultaneously observed on the spectral range of the spectrometer. Thus there is a need of optical probes of deterministic colors with non-overlapping narrow spectral signatures. In the recent past, a new class of biophotonic tool – called laser particles – has emerged, which is unique in terms of its narrow spectral linewidth arising from lasing. In the previous studies, however, the lasing wavelength of the individual laser particles has been random. In this presentation, we shall report the first step towards addressing this, by demonstrating fabrication of InGaAsP microdisk laser particles by active photoelectrochemical etching to lase in distinct deterministic narrow-band “colors”. The experimental pathway supported by a theoretical model to explain the results, would be presented. By careful choice of the material system coupled with the design of the disk geometry, we were able to demonstrate multiple unique “color bands” of single-mode lasing of all microdisks simultaneously emitting in the same narrow spectral band. Transferring these wavelength-tuned microdisks onto a PDMS substrate, we create free-standing precision laser particles. This technology could potentially open up a viable avenue to simultaneously identify and image over 20 unique biomarkers in the same spectral bandwidth as currently provided by 4 organic fluorophores.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Background and objectivesCompared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. MethodsTo fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. ResultsThe proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. ConclusionsThe proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

Improving hyperspectral image classification through spectral-spatial feature reduction with a hybrid approach and deep learning

ABSTRACT The Hyperspectral Image (HSI) is a great source of information for observing the earth’s elements due to its numerous narrow and continuous spectral wavelength bands. There are some key difficulties, such as the fact that the image bands are spectrally and spatially highly correlated, and the ‘curse of dimensionality’ in using the original HSI for classification. Band (dimensionality/feature) reduction is necessary to improve classification performance through feature extraction and selection. As such, this paper proposes a hybrid HSI classification paradigm, termed Hybrid-2DNET. Specifically, our proposed Hybrid-2DNET incorporates factor analysis-based feature extraction along the Minimum-Redundancy-Maximum-Relevance (mRMR)-based feature selection criterion and a 2D-wavelet Convolutional Neural Network (CNN) to reduce both spectral and spatial dimensionalities. The experimental analyses performed on three remote sensings HSI datasets, namely Indian Pines, University of Pavia, and Salinas Scene, manifest that our proposed Hybrid-2DNET outperforms the handcrafted and deep learning methods.

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Remote sensing is an effective way to analyze land surface changes on regular basis globally. In the previous literature, numerous change detection models were developed to detect the multitemporal or seasonal variations using different optical datasets, such as multispectral and hyperspectral. But there are many challenges involved in the processing of numerous bands, especially in the case of hyperspectral datasets, such as computational constraints and radiometric/atmospheric distortion. A simple framework-based artificial neural network (ANN) and post-classification comparison (PCC), named ANN-based PCC (ANPC), has been proposed to detect the multitemporal changes over agricultural land using hyperspectral, i.e., Earth Observation (EO-1) Hyperion and multispectral, i.e., Landsat-8. For cross-referencing, a comparative analysis is performed with respect to well-known PCC-based change detection methods, i.e., random forest-based post-comparison (RFPC) and support vector machine-based post-comparison (SVMPC). Experimental outcomes confirmed the effectiveness of ANPC (with an accuracy of more than 90%) in the extraction of multitemporal changes as compared to RFPC and SVMPC (with an accuracy of <90 % ). Our study enhances the utilization of the hyperspectral dataset (due to narrow spectral bands) in the extraction of critical information about the earth’s surface parameters.

Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming

This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas.

Thermoreflectance-based approach for surface temperature measurements of thin-film gold sensors.

A novel thermoreflectance-based diagnostic tool capable of visualizing spatial and temporal changes in surface temperature is presented. The method uses narrow spectral emission bands of blue [λ = 405nm with 10nm full-width-at-half-maximum (FWHM)] and green (λ = 532nm with 10nm FWHM) light to monitor the optical properties of gold and thin-film gold sensors, relating changes in reflectivity to temperature through a known calibration coefficient. The system is made robust to tilt and surface roughness variations through the simultaneous measurement of both probing channels with a single camera. Experimental validation is performed on two forms of gold materials heated from room temperature to 200 °C at a rate of ∼100 °C/min. Subsequent image analysis shows perceptible changes in reflectivity in the narrow band of green light, while the blue light remains temperature-insensitive. The reflectivity measurements are used to calibrate a predictive model with temperature-dependent parameters. The physical interpretation of the modeling results is given, and the strengths and limitations of the presented approach are discussed.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Double Deep Q-Network for Hyperspectral Image Band Selection in Land Cover Classification Applications

Hyperspectral data usually consists of hundreds of narrow spectral bands and provides more detailed spectral characteristics compared to commonly used multispectral data in remote sensing applications. However, highly correlated spectral bands in hyperspectral data lead to computational complexity, which limits many applications or traditional methods when applied to hyperspectral data. The dimensionality reduction of hyperspectral data becomes one of the most important pre-processing steps in hyperspectral data analysis. Recently, deep reinforcement learning (DRL) has been introduced to hyperspectral data band selection (BS); however, the current DRL methods for hyperspectral data BS simply remove redundant bands, lack the significance analysis for the selected bands, and the reward mechanisms used in DRL only take basic forms in general. In this paper, a new reward mechanism strategy has been proposed, and Double Deep Q-Network (DDQN) is introduced during BS using DRL to improve the network stabilities and avoid local optimum. To verify the effect of the proposed BS method, land cover classification experiments were designed and carried out to analyze and compare the proposed method with other BS methods. In the land cover classification experiments, the overall accuracy (OA) of the proposed method can reach 98.37%, the average accuracy (AA) is 95.63%, the kappa coefficient (Kappa) is 97.87%. Overall, the proposed method is superior to other BS methods. Experiments have also shown that the proposed method works not only for airborne hyperspectral data (AVIRIS and HYDICE), but also for hyperspectral satellite data, such as PRISMA data. When hyperspectral data is applied to similar applications, the proposed BS method could be a candidate for the BS preprocessing options.

Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications

Remote sensing is a technology that offers a unique opportunity of gathering land information by measuring and recording its emitted and reflected energy usually from a satellite or an aircraft. The capabilities of remote sensing satellite data in mapping, monitoring and managing land resources are intensifying with the rapid advancements in satellite technology. In addition, increased users demand in sustainable management of land resources has escalated the need for remote sensing technology. As a result, this article presents an overview of the remote sensing satellites that are best for mapping land resources and monitoring, focusing specifically on the necessary satellites, data availability and key land application areas. Currently, several remote sensing satellites are providing microwave, multispectral and hyperspectral data with a wide array of spatial, temporal and spectral resolutions used on land applications. Microwave remote sensing has seen the development of both active and passive remote sensing systems for remote sensing activities. Consequently, microwave data is now available with high spatial resolution and providing land information in all cloudy weather condition. On the other hand, optical remote sensing is providing space-based remote sensing data in a variety of spatial, spectral and temporal resolutions meeting the needs of many land applications. Similarly, hyperspectral remote sensing is providing digital imagery of earth resources in many narrow contiguous spectral bands. Additionally, other remote sensing techniques like Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) have helped in deriving detailed information of land resources to support land related studies. Besides having commercial satellites that are providing satellite data at a high cost, today several remote sensing data have been made available from open data sources and users can freely search and download areas of interest.

Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the "curse of dimensionality" problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.

Multiview Spatial–Spectral Two-Stream Network for Hyperspectral Image Unmixing

Linear spectral unmixing is an important technique in the analysis of mixed pixels in hyperspectral images. In recent years, deep learning-based methods have been garnering increasing attention in hyperspectral unmixing; especially, unsupervised autoencoder (AE) networks that have achieved excellent unmixing performance are a recent trend. While most approaches use spatial information, it is well known that hyperspectral data are characterized by a large number of narrow spectral bands. In order to take full advantage of the hyperspectral bands in unmixing and the spatial information, in this article, we explore multiview spectral and spatial information in an AE-based unmixing framework. We introduce multiview spectral information through spectral partitioning and propose a multiview spatial–spectral two-stream network, called MSSS-Net, which simultaneously learns a spatial stream network and a multiview spectral stream network in an end-to-end fashion for more efficient unmixing. The MSSS-Net is a two-stream deep unmixing network sharing a decoder, where its two AE networks employ recurrent neural networks (RNNs) to collaboratively utilize multiview spectral and spatial information. The spatial stream network branch extracts the spatial features of pixels and its neighbors, while the multiview spectral stream network branch exploits the multiview spectral bands of a pixel. Meanwhile, we design a cascaded bidirectional and unidirectional RNNs’ encoder structure for multiview spatial–spectral information to learn more discriminative deep patch-pixel features. Extensive ablation studies and experiments on both synthetic and real datasets demonstrate the superiority of the MSSS-Net over state-of-the-art unmixing methods.

Detection of Bastnäsite-Rich Veins in Rare Earth Element Ores Through Hyperspectral Imaging

Spectral indices are a well-established remote sensing tool showcased in many studies pertaining to fields such as agriculture, forest ecology, geology, soil sciences, vegetation, water resources, and urban development. In the case of geology, several indices already exist for the purpose of highlighting regions with interesting lithologies, such as iron oxides, aluminous clays, carbonates, and mafic to ultramafic minerals, based on signature absorption features in their reflectance spectra. However, in the literature, most indices often depend on the few, wide spectral bands used by spaceborne multispectral imagers such as Landsat 8 OLI, Terra ASTER, and Sentinel-2 MSI. Hyperspectral imagers are able to glean more detailed compositional information of a rock surface by operating with a myriad of narrow spectral bands, though the usage of these bands in spectral indices for mining and exploration purposes remains largely unexplored. We propose a new bastnäsite index capable of detecting the presence of rare earth elements and mapping their relative abundances across rock surfaces. Using four reference bands and four absorption bands in the visible to near infrared wavelength range, we apply this novel technique in a laboratory setting to ore samples extracted from the carbonatite body localized in the Sulfide Queen mine in Mountain Pass, California. Visual inspection is used to compare an index map to corresponding reflectance spectra for areas of known high and low rare earth element abundances, demonstrating its potential as a fast and straightforward means to assess rare earth element ore grade remotely.

Hyperspectral Image Denoising via Robust Subspace Estimation and Group Sparsity Constraint

Hyperspectral cameras capture electromagnetic information within hundreds of narrow spectral bands, producing hyperspectral images (HSIs) with the capability to accurately characterize the attribute information of objects. However, mixed noise induced by instrument and atmospheric effects hinders the interpretations and applications of the HSIs. In this paper, we propose a novel subspace representation based mixed noise removal method for hyperspectral images via Robust Subspace Estimation and weighted Group Sparsity constraint (RoSEGS). An outlier detection method is proposed to effectively detect sparse noise and replace the sparse noise with new estimates. A subspace estimation strategy, which is robust to mixed noise, is proposed. The subspace is first estimated after sparse noise detection and then optimized iteratively. In addition to the introduction of a state-of-the-art denoiser based on the plug-and-play technique to exploit self-similarity characteristics of the eigen-images, we impose a weighted group sparse regularization on the eigen-images to better promote the group sparsity of the spatial differences between the eigen-images, which further improves the denoising performance. We performed extensive experiments on two simulated and two real HSIs to fully demonstrate the effectiveness of the proposed method in comparison with seven state-of-the-art competitors.

Spectral correlation between wheat genotype replications over the visible and near-infrared spectrum

ABSTRACT The growth stage best suitable for wheat yield phenotyping has been a hot topic. This study provides a fresh insight into it, from the perspective of spectral correlation between wheat genotype replications. A number of 340 wheat genotypes and their replication were distributed in separate parts (west and east) of an experiment field (2019–2020) in Ashland, Kansas, USA. Unmanned aerial vehicle based hyperspectral images (400–100 nm) of the experiment field were taken over the late growing season, on 29 May 2020, 5 June 2020, and 12 June 2020, respectively. For each narrow spectral band, we calculated a coefficient of determination (R 2) between the reflectance of genotype replications. Results suggest that R 2 is relatively stable within visible spectrum (450–700 nm) and within near-infrared (NIR) spectrum (770–1000 nm), though it tends to be higher for the visible bands. Moreover, while the R 2 of the visible bands decreases across the three dates, it increases for the near-infrared bands. These findings suggest that genetic information is better reflected in visible reflectance than in near-infrared reflectance. Among the three dates, the one when highest intra-genotype spectral correlation over visible spectrum was observed might be the best timing to discriminate yield levels of different genotypes.

Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

Hyperspectral images (HSIs) have recently been exploited in several aspects as HSIs contain many contiguous and narrow discriminative spectral bands. The problem of dimensionality is a significant dilemma for HSIs due to there being plenty of irrelevant and redundant spectral bands and highly correlated bands that lead to Hughes phenomenon. To this end, we present an approach to selecting the most informative and relevant spectral bands for HSI dimensionality reduction using the Krill Herd (KH) algorithm. Moreover, KH is a heuristic search method that seeks to reach the optimum global solution within the search space and effectively evade falling into the local optima. Then an edge-preserving filter was employed to extract the spatial features while reducing noise and obtaining a suitable smoothing that improves the classification performance. Finally, the support vector machine classifier was performed at the pixel level for HSI classification. Furthermore, the proposed work was compared with the harmony search, genetic algorithm, bat algorithm, particle swarm optimization, and firefly algorithm. The experimental results demonstrated outstanding performance with an overall accuracy equal to 96.54%, 98.93%, 99.78%, and 98.66% on four hyperspectral datasets: Indian Pines scene, Pavia University scene, Salinas scene, and Botswana scene, respectively.

New Generation Hyperspectral Sensors DESIS and PRISMA Provide Improved Agricultural Crop Classifications

Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the Earth. Therefore, our overarching goal in this study was to use these data to explore advances that can be made in agricultural research. We selected PRISMA and DESIS images during the 2020 growing season in California's Central Valley to study seven major crops. PRISMA and DESIS images were highly correlated (R 2of 0.9–0.95). Out of the 235 DESIS bands (400–1000 nm) and 238 PRISMA bands (400–2500 nm), 26 (11%) and 45 (19%) bands, respectively, were optimal to study agricultural crops. These optimal bands provided crop type classification accuracies of 83–90%. Hyperspectral vegetation indices to estimate plant pigment content, stress, biomass, moisture, and cellulose/lignin content were also identified.

Optical Measurements of Surface Topography for Materials with Scattering Light Reflection by Images Processing using Origin Lab

A method of non-contact optical measurements of surfaces topologies for materials with scattering light reflection using OriginLab images module processing is described. The images were captured under high lighting levels and close distances at photographing. Digital images were converted by OriginLab software into a data matrix, each element of which contains information about the level of reflected light from individual sections of the surface with different depths. Than the image data was converted into worksheet data and calculated to data with real geometric parameters. The length, width and depth of can be measured with special image OriginLab tools The processing of hyper spectral images obtained using a very narrow spectral band (through filters of Asahi Spectra Co.Ltd) with proposed optical method makes possibilities for determination geometrical parameters of damages surface biological objects. Different surfaces with scattering light reflection from 0.1 mm till 10 cm is analyzed. Keywords— scattering light reflection, image processing, optical measurement, geometric parameters.