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Journal articles on the topic 'Multi-spectral sensor'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Soria Poma, Xavier. "Single Sensor Multi-Spectral Imaging." ELCVIA Electronic Letters on Computer Vision and Image Analysis 18, no. 2 (2020): 11. http://dx.doi.org/10.5565/rev/elcvia.1194.

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Gasmi, Anis, Cécile Gomez, Abdelghani Chehbouni, Driss Dhiba, and Hamza Elfil. "Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches." Remote Sensing 14, no. 5 (2022): 1103. http://dx.doi.org/10.3390/rs14051103.

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Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Senti

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Demars, Casey, Michael Roggemann, Adam Webb, and Timothy Havens. "Target Localization and Tracking by Fusing Doppler Differentials from Cellular Emanations with a Multi-Spectral Video Tracker." Sensors 18, no. 11 (2018): 3687. http://dx.doi.org/10.3390/s18113687.

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We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors

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Wu, Haibin, Shiyu Dai, Chengyang Liu, Aili Wang, and Yuji Iwahori. "A Novel Dual-Encoder Model for Hyperspectral and LiDAR Joint Classification via Contrastive Learning." Remote Sensing 15, no. 4 (2023): 924. http://dx.doi.org/10.3390/rs15040924.

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Deep-learning-based multi-sensor hyperspectral image classification algorithms can automatically acquire the advanced features of multiple sensor images, enabling the classification model to better characterize the data and improve the classification accuracy. However, the currently available classification methods for feature representation in multi-sensor remote sensing data in their respective domains do not focus on the existence of bottlenecks in heterogeneous feature fusion due to different sensors. This problem directly limits the final collaborative classification performance. In this

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Handcock, R. N., D. L. Gobbett, L. A. González, G. J. Bishop-Hurley, and S. L. McGavin. "Combining multi-spectral proximal sensors and digital cameras for monitoring grazed tropical pastures." Biogeosciences Discussions 12, no. 21 (2015): 18007–51. http://dx.doi.org/10.5194/bgd-12-18007-2015.

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Abstract. Timely and accurate monitoring of pasture biomass and ground-cover is necessary in livestock production systems to ensure productive and sustainable management of forage for livestock. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since such sensors can return data in near real-time, and have the potential to be deployed on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. However, there are unresolved challenges in developing calibrations to convert raw sensor data t

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Canisius, Francis, Shusen Wang, Holly Croft, et al. "A UAV-Based Sensor System for Measuring Land Surface Albedo: Tested over a Boreal Peatland Ecosystem." Drones 3, no. 1 (2019): 27. http://dx.doi.org/10.3390/drones3010027.

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A multiple sensor payload for a multi-rotor based UAV platform was developed and tested for measuring land surface albedo and spectral measurements at user-defined spatial, temporal, and spectral resolutions. The system includes a Matrice 600 UAV with an RGB camera and a set of four downward pointing radiation sensors including a pyranometer, quantum sensor, and VIS and NIR spectrometers, measuring surface reflected radiation. A companion ground unit consisting of a second set of identical sensors simultaneously measure downwelling radiation. The reflected and downwelling radiation measured by

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Liu, Cong, Lin Wang, Xuetong Fu, et al. "Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data." Agriculture 15, no. 13 (2025): 1425. https://doi.org/10.3390/agriculture15131425.

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The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy sp

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Brown, S. W., and P.-S. Shaw. "A Method for the Reduction of Out-of-Band Measurement Errors in Multi-band Instruments using Synthetic Source Distributions." Journal of Physics: Conference Series 2149, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2149/1/012018.

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Abstract A method to reduce multi-band sensor measurement biases due to finite out-of-band response is described. The method takes advantage of the fact that out-of-band measurement errors cancel if the calibration source and the measured source have the same spectral distributions—independent of their spectral distributions or the magnitude of a sensor band’s out-of-band response. Using a known spectral responsivity, a synthetic, arbitrary source spectral distribution can replace a realized spectral distribution in the measurement equation and the signal can be calculated rather than measured

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MILNE, JASON S., JOHN M. DELL, ADRIAN J. KEATING, and LORENZO FARAONE. "OPTICAL MEMS TECHNOLOGIES FOR ELECTRICALLY TUNABLE MULTI-SPECTRAL SHORT-WAVE INFRARED SENSORS AND ARRAYS." International Journal of High Speed Electronics and Systems 18, no. 04 (2008): 1035–44. http://dx.doi.org/10.1142/s0129156408005989.

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Optical MEMS technology combined with broadband infrared sensor technology is used to realize wavelength-tunable infrared sensors. This paper describes the ongoing research into one such sensor design based on an electrically tunable Fabry-Pérot cavity. Theory, measured results and future research directions are presented and discussed for the single-sensor design currently being developed, in the context of the intended application of this technology; the development of lightweight, portable and robust multi-spectral imaging systems.

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Li, Dong, Lu, Lou, and Zhou. "Multi-Sensor Face Registration Based on Global and Local Structures." Applied Sciences 9, no. 21 (2019): 4623. http://dx.doi.org/10.3390/app9214623.

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The work reported in this paper aims at utilizing the global geometrical relationship and local shape feature to register multi-spectral images for fusion-based face recognition. We first propose a multi-spectral face images registration method based on both global and local structures of feature point sets. In order to combine the global geometrical relationship and local shape feature in a new Student’s t Mixture probabilistic model framework. On the one hand, we use inner-distance shape context as the local shape descriptors of feature point sets. On the other hand, we formulate the feature

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Kulo, Nedim. "Multisensor Remote Sensing Data Integration." Geodetski glasnik, no. 50 (December 31, 2019): 71–94. http://dx.doi.org/10.58817/2233-1786.2019.53.50.71.

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The integration of remote sensing data involves combining various data to get better information, or more information about an area or phenomenon of interest. When it comes to combining data, it usually refers to multi-hour, multi-resolution or multi-sensor data linking. The subject of multi-sensor data integration is the combining of data collected by different sensors. A common example of this type of integration is the integration of multispectral optical data with radar imagery. Both spectrally different modes of representation complement each other: optical data is ''in charge'' of detail

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Ramirez, Juan Marcos, and Henry Arguello. "Spectral Image Classification From Multi-Sensor Compressive Measurements." IEEE Transactions on Geoscience and Remote Sensing 58, no. 1 (2020): 626–36. http://dx.doi.org/10.1109/tgrs.2019.2938724.

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Fathy, Alaa, Martine Gnambodoe-Capochichi, Yasser M. Sabry, et al. "Potential of a Miniature Spectral Analyzer for District-Scale Monitoring of Multiple Gaseous Air Pollutants." Sensors 23, no. 14 (2023): 6343. http://dx.doi.org/10.3390/s23146343.

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Gas sensors that can measure multiple pollutants simultaneously are highly desirable for on-site air pollution monitoring at various scales, both indoor and outdoor. Herein, we introduce a low-cost multi-parameter gas analyzer capable of monitoring multiple gaseous pollutants simultaneously, thus allowing for true analytical measurement. It is a spectral sensor consisting of a Fourier-transform infrared (FTIR) gas analyzer based on a mid-infrared (MIR) spectrometer. The sensor is as small as 7 × 5 × 2.5 cm3. It was deployed in an open-path configuration within a district-scale climatic chamber

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Rafiezadeh Shahi, Kasra, Pedram Ghamisi, Behnood Rasti, Robert Jackisch, Paul Scheunders, and Richard Gloaguen. "Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm." Remote Sensing 12, no. 23 (2020): 4007. http://dx.doi.org/10.3390/rs12234007.

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The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-senso

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Lorenz, Sandra, Peter Seidel, Pedram Ghamisi, et al. "Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction." Sensors 19, no. 12 (2019): 2787. http://dx.doi.org/10.3390/s19122787.

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Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image

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Moreno, Marco, Sagar Dalai, Grace Cott, et al. "Multi-Camera Machine Learning for Salt Marsh Species Classification and Mapping." Remote Sensing 17, no. 12 (2025): 1964. https://doi.org/10.3390/rs17121964.

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Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our

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Fu, Hongyu, Jianfu Chen, Jianning Lu, et al. "A Comparison of Different Remote Sensors for Ramie Leaf Area Index Estimation." Agronomy 13, no. 3 (2023): 899. http://dx.doi.org/10.3390/agronomy13030899.

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Leaf area index (LAI) is an important parameter indicating crop growth. At present, spectral technology has developed into a popular means for LAI monitoring, which can provide accurate estimation results by constructing a model with crop spectral information and a ground LAI value. Spectral range and data type may affect the performance of the model, but few studies have compared the estimation accuracy of crop LAI using different spectral sensors, especially in ramie. In this study, we compared the potential to estimate the ramie LAI of a hyperspectral sensor with a multispectral sensor. A h

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Gerhards, Max, Martin Schlerf, Kaniska Mallick, and Thomas Udelhoven. "Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review." Remote Sensing 11, no. 10 (2019): 1240. http://dx.doi.org/10.3390/rs11101240.

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Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions

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KUMAR, A. B., K. N. TIWARI, R. S. DWIVEDI, and D. KARUNAKAR. "SPECTRAL BEHAVIOUR AND SPECTRAL SEPARABILITY OF ERODED LANDS USING MULTI-SENSOR DATA." Land Degradation & Development 8, no. 1 (1997): 27–38. http://dx.doi.org/10.1002/(sici)1099-145x(199703)8:1<27::aid-ldr231>3.0.co;2-q.

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Sugianto, Sugianto, and Shawn Laffan. "Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation." Aceh International Journal of Science and Technology 1, no. 1 (2012): 30–39. http://dx.doi.org/10.13170/aijst.1.1.12.

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Abstract - The surface reflectance anisotropy can be estimated by directional reflectance analysis through the collection of multi-angular spectral data. Proper characterization of the surface anisotropy is an important element in the successful interpretation of remotely sensed signals. A signal received by a sensor from a vegetation canopy is affected by several factors. One of them is the sensor zenith angle. Functional data analysis can be used to assess the distribution and variation of spectral reflectance due to sensor zenith angle. This paper examines the effect of sensor zenith angles

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21

Li, Rong, Jian Liu, and Xiaojun Huang. "Passive and Battery-Free UWB Sensor with Multiple Digital Bits Based on Spectral–Temporal Joint Coding." Electronics 14, no. 4 (2025): 671. https://doi.org/10.3390/electronics14040671.

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In this paper, a passive wireless sensor is designed and developed specifically for a wireless sensing system required by multi-bit applications. The proposed sensor is abided by the formula of UWB spectrum ranging from 3.1 GHz to 10.6 GHz band, and the capability of carrying multiple digital bits can be realized by the combination of multiple sensor units that are operated in the principle of Spectral–Temporal Joint Coding and Modulation. A prototype of such a sensor is configured by four such kinds of UWB sensor units, each of which is functionalized by modulating UWB pulse in the time domai

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Vangi, Elia, Giovanni D’Amico, Saverio Francini, et al. "The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination." Sensors 21, no. 4 (2021): 1182. http://dx.doi.org/10.3390/s21041182.

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Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be emplo

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SHENG, Tao, Wenyan XU, Shengzhe SHI, and Sheng LIU. "Turbidity measurement based on a multi-wavelength spectral sensor." Journal of Measurement Science and Instrumentation 13, no. 2 (2022): 147–55. http://dx.doi.org/10.62756/jmsi.1674-8042.2022017.

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Milella, A., M. Nielsen, and G. Reina. "Sensing in the visible spectrum and beyond for terrain estimation in precision agriculture." Advances in Animal Biosciences 8, no. 2 (2017): 423–29. http://dx.doi.org/10.1017/s2040470017000152.

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A multi-sensor approach for terrain estimation is proposed using a combination of complementary optical sensors that cover the visible (VIS), near infrared (NIR) and infrared (IR) spectrum. The sensor suite includes a stereovision sensor, a VIS-NIR camera and a thermal camera, and it is intended to be mounted on board an agricultural vehicle, pointing downward to scan the portion of the terrain ahead. A method to integrate the different sensor data and create a multi-modal dense 3D terrain map is presented. The stereovision input is used to generate 3D point clouds that incorporate RGB-D infor

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Habibullah, Mohammad, Mohammad Reza Mohebian, Raju Soolanayakanahally, et al. "Low-Cost Multispectral Sensor Array for Determining Leaf Nitrogen Status." Nitrogen 1, no. 1 (2020): 67–80. http://dx.doi.org/10.3390/nitrogen1010007.

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A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture th

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Shin, Jisun, Keunyong Kim, Young Son, and Joo-Hyung Ryu. "Synergistic Effect of Multi-Sensor Data on the Detection of Margalefidinium polykrikoides in the South Sea of Korea." Remote Sensing 11, no. 1 (2018): 36. http://dx.doi.org/10.3390/rs11010036.

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Since 1995, Margalefidinium polykrikoides blooms have occurred frequently in the waters around the Korean peninsula. In the South Sea of Korea (SSK), large-scale M. polykrikoides blooms form offshore and are often transported to the coast, where they gradually accumulate. The objective of this study was to investigate the synergistic effect of multi-sensor data for identifying M. polykrikoides blooms in the SSK from July 2018 to August 2018. We found that the Spectral Shape values calculated from in situ spectra and M. polykrikoides cell abundances in the SSK were highly correlated. Comparing

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Gutiérrez, Salvador, Alexander Wendel, and James Underwood. "Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation." Computers and Electronics in Agriculture 164, no. 1 (2019): 104890. https://doi.org/10.5281/zenodo.10642927.

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Hyperspectral imaging (HSI) is a powerful technology already used for many objectives in agriculture. Applications include disease monitoring, plant phenotyping, yield estimation or fruit composition and ripeness. However, the cost of hyperspectral sensors is typically an order of magnitude higher than simpler RGB cameras, which can be prohibitive. Given that in HSI processing the spectral data often contains redundancies, the full spectra are not always required for a specific application and there is an opportunity to design a lower cost multi-spectral sensing system by dimensionality reduct

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Li, Yan, Xuerui Qi, Yucheng Cai, et al. "A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot." Remote Sensing 16, no. 24 (2024): 4725. https://doi.org/10.3390/rs16244725.

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Automated monitoring of the rice leaf area index (LAI) using near-ground sensing platforms, such as inspection robots, is essential for modern rice precision management. These robots are equipped with various complementary sensors, where specific sensor capabilities partially overlap to provide redundancy and enhanced reliability. Thus, leveraging multi-sensor fusion technology to improve the accuracy of LAI monitoring has become a crucial research focus. This study presents a rice LAI monitoring model based on the fused data from RGB and multi-spectral cameras with an ensemble learning algori

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Zhang, Yu, Da Xu, Bin Zhao, et al. "Multi-Partition Mapping Simulation Method for Stellar Spectral Information." Photonics 12, no. 6 (2025): 585. https://doi.org/10.3390/photonics12060585.

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Stellar radiation simulation is critical in the space industry; however, with the current simulation methods, only a single color temperature and magnitude can be modulated at a time. Furthermore, star sensors rely on star observation tests for accurate calibration; this seriously restricts their development. This paper presents a novel star spectral information multi-partition mapping simulation method to closely simulate real sky star map information, thus replacing non-scenario-specific field stargazing experiments. First, using the stellar spectral simulation principle, a multi-partition m

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Toivonen, Mikko E., and Arto Klami. "Practical Camera Sensor Spectral Response and Uncertainty Estimation." Journal of Imaging 6, no. 8 (2020): 79. http://dx.doi.org/10.3390/jimaging6080079.

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Knowledge of the spectral response of a camera is important in many applications such as illumination estimation, spectrum estimation in multi-spectral camera systems, and color consistency correction for computer vision. We present a practical method for estimating the camera sensor spectral response and uncertainty, consisting of an imaging method and an algorithm. We use only 15 images (four diffraction images and 11 images of color patches of known spectra to obtain high-resolution spectral response estimates) and obtain uncertainty estimates by training an ensemble of response estimation

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Lopin, Prattana, Pichapob Nawsang, Srisangwan Laywisadkul, and Kyle V. Lopin. "Evaluation of Low-Cost Multi-Spectral Sensors for Measuring Chlorophyll Levels Across Diverse Leaf Types." Sensors 25, no. 7 (2025): 2198. https://doi.org/10.3390/s25072198.

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Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure r

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Li, Wei. "Unmanned Aerial Vehicle (UAV) in Precision Agriculture to Identify the Crop Water Shortage by Using Multi-Spectral Sensor." Open Access Journal of Agricultural Research 8, no. 2 (2023): 1–4. http://dx.doi.org/10.23880/oajar-16000303.

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Accurate diagnosis of crop water shortage and scientific irrigation decisions are crucial. A new method of multi-spectral imaging remote sensing image extraction of tea canopy temperature is proposed, and an automatic processing system of remote sensing thermal imaging images is established. In this short communication, A UAV (Unmanned Aerial Vehicle) with multi-spectral sensors used to capture the images. The research results show that the system can efficiently mosaic images without image gap, and ensure that the soil background is wholly eliminated. This research gives us new methods to set

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Chen, Jie, Kai Xiao, Kai You, Feng Duan, and Xianguo Qing. "Multi-Feature Multi-Sensor Fusion for Emitter Identification Based on a Modified DS Application." Journal of Sensors 2022 (April 20, 2022): 1–21. http://dx.doi.org/10.1155/2022/4264057.

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Emitter identification is a crucial task in electronic countermeasure technology area, which deeply affects the accuracy of subsequent threat estimation. In emitter identification system, sensors (transmitter and receiver) have inevitable inconsistency and fuzziness, along with possible ambiguity and instability under interference and malfunction. To manage the uncertainty in emitter identification system, we propose a multi-feature multi-sensor fusion algorithm based on a modified DS application. The modified DS application for emitter identification system is accomplished by two parts—multi-

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Zhihua, Li, Zhou Xucheng, Zou Xiaobo, et al. "Characterization of colorimetric sensor arrays by a multi-spectral technique." Analytical Methods 8, no. 11 (2016): 2357–65. http://dx.doi.org/10.1039/c5ay03255e.

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Yan, Xiaojian, Yao Tan, Yi Wang, et al. "Near-Infrared Spectral MEMS Gas Sensor for Multi-Component Food Gas Detection." Micromachines 16, no. 2 (2025): 135. https://doi.org/10.3390/mi16020135.

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The complex application environments of gas detection, such as in industrial process monitoring and control, atmospheric and environmental monitoring, and food safety, require real-time and online high-sensitivity gas detection, as well as the accurate identification and quantitative analysis of gas samples. Despite the progress in gas analysis and detection methods, high-precision and high-sensitivity detection requirements for target gases of multiple components in mixed gases are still challenging. Here, we demonstrate a micro-electromechanical system (MEMS) with near-infrared (NIR) spectra

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Nandy, Avik, Stuart Phinn, Alistair Grinham, and Simon Albert. "Developing a Semi-Automated Near-Coastal, Water Quality-Retrieval Process from Global Multi-Spectral Data: South-Eastern Australia." Remote Sensing 16, no. 13 (2024): 2389. http://dx.doi.org/10.3390/rs16132389.

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The estimation of water quality properties through satellite remote sensing relies on (1) the optical characteristics of the water body, (2) the resolutions (spatial, spectral, radiometric and temporal) of the sensor and (3) algorithm(s) applied. More than 80% of global water bodies fall under Case I (open ocean) waters, dominated by scattering and absorption associated with phytoplankton in the water column. Globally, previous studies show significant correlations between satellite-based retrieval methods and field measurements of absorbing and scattering constituents, while limited research

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Zhao, Ling Jun, Wan Feng Zhang, Li Fang Zhang, and Ji Bo Xie. "Study on Alteration Information Extraction by Using Assimilation of Multi-Sensor Spectral Data." Applied Mechanics and Materials 241-244 (December 2012): 943–47. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.943.

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Some alterations of similar spectral reflectances cannot be distinguished accurately for their lower spectral resolution when the traditional methods, such as, band ratio and principal component analysis are used to extract alteration information from Landsat ETM multi-spectral data. In this paper, the band1~band7 of MODIS whose wave lengths are among 10~500nm, together with ETM’s multi-spectral bands, whose spatial resolutions are 30m, are chosen in the execution of data assimilation. After the third order wavelet transformation, the low-frequency component of ETM data are replaced by the MOD

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Rekatsinas, Christoforos S., and Dimitris A. Saravanos. "A time domain spectral layerwise finite element for wave structural health monitoring in composite strips with physically modeled active piezoelectric actuators and sensors." Journal of Intelligent Material Systems and Structures 28, no. 4 (2016): 488–506. http://dx.doi.org/10.1177/1045389x16649700.

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A new explicit, two-dimensional plane strain, time domain spectral finite element is developed to enhance the simulation of guided waves generated by active piezoelectric sensors in laminated composite strips. A new multi-field layerwise theory is formulated for composite laminates with piezoelectric actuators and sensors which captures straight-crested symmetric and anti-symmetric Lamb waves. Third-order Hermite polynomial splines are employed for the approximation of displacements and electric potential through the thickness, and the piezoelectric actuators and sensors are physically modeled

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Jeon, Taehyeong, Proloy Taran Das, Mijin Kim, et al. "Operational Parameters for Sub-Nano Tesla Field Resolution of PHMR Sensors in Harsh Environments." Sensors 21, no. 20 (2021): 6891. http://dx.doi.org/10.3390/s21206891.

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The resolution of planar-Hall magnetoresistive (PHMR) sensors was investigated in the frequency range from 0.5 Hz to 200 Hz in terms of its sensitivity, average noise level, and detectivity. Analysis of the sensor sensitivity and voltage noise response was performed by varying operational parameters such as sensor geometrical architectures, sensor configurations, sensing currents, and temperature. All the measurements of PHMR sensors were carried out under both constant current (CC) and constant voltage (CV) modes. In the present study, Barkhausen noise was revealed in 1/f noise component and

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Chniouar, Marouane, Amina Wafik, Youssef Daafi, and Daniela Guglietta. "Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery." Mining 4, no. 2 (2024): 302–25. http://dx.doi.org/10.3390/mining4020018.

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This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbo

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Tan Jun Wei, Lip Zhan Hong, Sulaiman Wadi Harun, and Cheng Xiau San. "PANDA Fiber Sagnac Interferometer for Temperature Sensor Application." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 3 (2023): 316–23. http://dx.doi.org/10.37934/araset.32.3.316323.

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Fiber Sagnac loop mirror has great potential for use in various application such as multi-wavelength laser source, strain and temperature sensors. This paper demonstrates an optical fiber temperature sensor based on two lengths of PANDA polarization maintaining fibers (PMFs) in a fiber Sagnac interferometer. The temperature sensing is based on the change of spectral spacing of the generated optical comb with the temperature difference between two PMFs. The performance of the system was characterized numerically and experimentally by measuring the spectral spacing when raising the temperature o

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Zhao, Yu, Thierry Camps, Véronique Bardinal, and Julien Perchoux. "Optical Feedback Interferometry Based Microfluidic Sensing: Impact of Multi-Parameters on Doppler Spectral Properties." Applied Sciences 9, no. 18 (2019): 3903. http://dx.doi.org/10.3390/app9183903.

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As a compact and simple sensing technique, optical feedback interferometry (OFI) can be a promising flowmetry method in various microfluidic applications. In this paper, OFI-based flowmetry sensor performance in a microscale flow scheme is studied theoretically and experimentally. An innovating model and different numerical methods are investigated, where the scattering light angle distribution is involved to predict the Doppler frequency distribution. For the first time, our model describes the influences of multiple OFI sensor system characteristics, such as flowing particle size, concentrat

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R. Sui, J. B. Wilkerson, W. E. Hart, L. R. Wilhelm, and D. D. Howard. "MULTI-SPECTRAL SENSOR FOR DETECTION OF NITROGEN STATUS IN COTTON." Applied Engineering in Agriculture 21, no. 2 (2005): 167–72. http://dx.doi.org/10.13031/2013.18148.

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Muhiyudin, Manu, David Hutson, Desmond Gibson, Ewan Waddell, Shigeng Song, and Sam Ahmadzadeh. "Miniaturised Infrared Spectrophotometer for Low Power Consumption Multi-Gas Sensing." Sensors 20, no. 14 (2020): 3843. http://dx.doi.org/10.3390/s20143843.

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Concept, design and practical implementation of a miniaturized spectrophotometer, utilized as a mid-infrared-based multi gas sensor is described. The sensor covers an infrared absorption wavelength range of 2.9 to 4.8 um, providing detection capabilities for carbon dioxide, carbon monoxide, nitrous oxide, sulphur dioxide, ammonia and methane. A lead selenide photo-detector array and customized MEMS-based micro-hotplate are used as the detector and broadband infrared source, respectively. The spectrophotometer optics are based on an injection moulded Schwarzschild configuration incorporating op

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Salau, Ayodeji Olalekan, Shruti Jain, and Joy Nnenna Eneh. "A review of various image fusion types and transforms." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1515–22. https://doi.org/10.11591/ijeecs.v24.i3.pp1515-1522.

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Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of resul

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Handique, B. K., C. Goswami, C. Gupta, et al. "HIERARCHICAL CLASSIFICATION FOR ASSESSMENT OF HORTICULTURAL CROPS IN MIXED CROPPING PATTERN USING UAV-BORNE MULTI-SPECTRAL SENSOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 67–74. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-67-2020.

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Abstract. Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state loca

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Grybas, Heather, and Russell G. Congalton. "A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests." Remote Sensing 13, no. 13 (2021): 2631. http://dx.doi.org/10.3390/rs13132631.

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Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a s

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Chhapariya, Koushikey, Emmett Ientilucci, Krishna Mohan Buddhiraju, and Anil Kumar. "Target Detection and Characterization of Multi-Platform Remote Sensing Data." Remote Sensing 16, no. 24 (2024): 4729. https://doi.org/10.3390/rs16244729.

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Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing ha

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Chen, F., S. Lou, Q. Fan, J. Li, C. Wang, and M. Claverie. "A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (May 2, 2018): 2619–24. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2619-2018.

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A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land

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Mao, Jinghua, Yongmei Wang, Entao Shi, and Jinduo Wang. "Pre-Launch Spectral Calibration of the Absorbed Aerosol Sensor." Sensors 23, no. 20 (2023): 8590. http://dx.doi.org/10.3390/s23208590.

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Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument’s spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function,

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