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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery V"

共 529 条记录，以下是31-40 订阅

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Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections 27

Reducing false alarms in hyperspectral images using a covari...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Ben Shabat, Idan Zinger, Lihi Rotman, Stanley R. Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva Israel

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

In image processing, the Matched Filter algorithm uses the estimated covariance matrix to give each pixel a score based on the similarity between the pixel and the signature of the target. While using this target detection algorithm, false alarms are inevitable. In order to solve this problem, a method using an iterative process to produce a second covariance matrix which only uses the most likely false alarms was presented [6]. In this paper, we test this method, attempt to improve it, and expand on the cases in which it is the most effective. In all cases, the new method showed a decrease in false alarms, and in some cases a decrease of over 85%.

关键词： variance covariance matrix False alarms Covariance matrix pixel hyperspectral imagery Iterative Process

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Hardware Implementation of Lossless hyperspectral Compression USING CCSDS 123.0.B-1 11

Hardware Implementation of Lossless Hyperspectral Compressio...

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11th IEEE International conference on Communication Systems and Network Technologies, CSNT 2022

作者： Neelima, v. Sowmya, P. Shantala, H. Chayan, D. Srividya, S. Jothy, S. vanitha, M. Ursc Isro Payload Data Management Group Bengaluru India

ISBN: (纸本)9781665480383

Remote sensing satellites with hyperspectral and multispectral payloads would require large data storage onboard and downlink bandwidth because the data volume collected in a spatial area has multiple spectral band data. With remote sensing satellites collecting hyper-spectral images, compression algorithms are required for efficient utilization of downlink bandwidth. Since increasing the storage or bandwidth is expensive onboard, data compression techniques are utilized for effective utilization of resources. With the knowledge of elemental absorption and reflectance characteristics, spectroscopy aids in identification and analysis of the target. multispectral or hyperspectral imaging sensors capture the target spatial area in multiple bands. The resolution of bands is much larger (of the order of few 100nm) in multispectral bands. Band gap between adjacent bands in hyper spectral imaging is in 10nm range. Thus formed spectral cube has length and breadth defining the spatial coordinates while the depth of it defines the number of bands. Consultative Committee for Space Data Systems (CCSDS) recommends certain algorithms for lossless and lossy compression which is selected based on the application. This paper details about implementation of lossless compression algorithm defined in CCSDS 123.0-B-1 using predictor and block adaptive entropy coder. The implementation is done on space grade FPGA RTG4150 with an operation frequency of 89.694 MHz with varying pixel depth from 12 to 16bits. This gives a throughput more than 1Gbps. © 2022 IEEE.

关键词： Bandwidth

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The Underground Surface Analysis of Waste Disposal Objects Based on the Neural Network Image Processing Methods 28

The Underground Surface Analysis of Waste Disposal Objects B...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvIII

作者： Kazaryan, M. Semenishchev, E. voronin, v. North Ossetian State Med Acad Vladikavkaz Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (纸本)9781510650657;9781510650640

Remote sensing of the Earth allows to receive medium information, a high spatial resolution from space vehicles, and to conduct hyperspectral measurements. This study presents a remote sensing application using time-series Landsat satellite images to monitor the solid waste disposal site (WDS). In this work, neural network image processing methods are used as part of the proposed algorithm for computer modeling of the fractal-percolation process of the underlying surface filtrate in remote sensing imaging. Investigations related to the analysis of the filtrate and decryption of space images are carried out using the apparatus of discrete orthogonal transformations and two convolutional neural networks. The first neural network detects a waste disposal facility, the second works to localize the area of the leachate identified based on the first network. The results obtained can serve as a basis for developing a methodology for assessing the effectiveness of measures to neutralize the underlying surface of waste disposal sites from leachate and its seepage into the soil using remote sensing technologies. This technique can become the object of further research on developing a medical-prophylactic expert system at the territorial level for the detection and neutralization of unauthorized waste disposal sites based on medium and high-resolution space images. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

关键词： waste disposal facility landfill chemical process percolation waste degradation filtrate neural network

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Modeling the Radiative Response of a High Facet Count Rainforest for Synthetic Sensor imagery 27

Modeling the Radiative Response of a High Facet Count Rainfo...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Hunter, Robert H. White, Barry C. Patel, Reena R. Ballard, Jerrell R. US Army Engineer Res & Dev Ctr Vicksburg MS 39180 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Computational modeling of spectral and hyperspectral imagery can be performed using radiative flux calculations on highly resolved geometric models. Faceted geometry models are both memory intensive and computationally expensive but allow for a fine-grained approach to radiative modeling. Using high resolution faceted geometry, improved synthetic imagery can be generated from a ray casting sensor model. This paper describes the results of a distributed memory ray tracing architecture for processing high facet count geometry that is capable of modeling radiative flux for highly resolved landscapes. Monte Carlo integration of the radiative transfer equation is coupled with a soil heat transfer model to facilitate solving for temperatures. Ray tracing procedures then use material properties to communicate radiative flux back to a sensor model. Emitted radiation along with mid-wave radiation reflected from neighboring facets and reflected short-wave solar radiation is computed and returned for rays cast from a sensor model. Radiative results of a prototype rainforest have been acquired that demonstrate the modeling capability of the architecture for geometries exceeding 40 million facets. Images of individual spectral components visually validate the legitimacy of the flux simulation. This paper presents an architecture that has been developed with the potential to produce quality synthetic spectral data based on modeling of actual temperature and radiative flux.

关键词： High Performance Computing Synthetic Data multispectral Rendering

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veritas: an admissible detector for targets of unknown strength 27

<i>Veritas</i>: an admissible detector for targets of unknow...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： Theiler, James Los Alamos Natl Lab Space Data Sci & Syst Grp Los Alamos NM 87545 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Inspired by the comic faux-Latin aphorism "veritas duo sigma" (truth at two sigmas), an approach to target detection is proposed based on a likelihood ratio test in which the unknown target strength is treated as known, with strength chosen to correspond to a minimal level of detectability. For Gaussian distributions, this strength typically corresponds to two or three sigmas. This detector is admissible, which means that there is no other detector that is uniformly superior to it. The simplicity of the veritas detector permits closed-form solutions to be derived for a variety of signal detection problems. In a series of numerical experiments, these simple detectors are compared to traditional detectors, such as the locally most powerful detector and the generalized likelihood ratio test detector.

关键词： Adaptive matched filter Clutter Clairvoyant fusion Composite hypothesis testing Elliptically-contoured distribution Generalized likelihood ratio test hyperspectral imagery Target detection

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Improving mosquito population models over the Greater Toronto Area using MSI and SAR data 27

Improving mosquito population models over the Greater Toront...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXvII

作者： DeMets, Sydney A. Ziemann, Amanda Manore, Carrie Russell, Curtis Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Analyt Intelligence & Technol Div Los Alamos NM USA Publ Hlth Ontario Toronto ON Canada

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

As West Nile virus (WNv) and St. Louis Encephalitis (SLE) become more prevalent across North America, there is an increased risk of fatal neuroinvasive cases. In order for public health officials to prepare for these cases and potentially intervene, the ability to forecast mosquito borne disease outbreaks is paramount. In practice, however, such vector borne diseases are notoriously difficult to predict due to their seemingly sporadic spatial and temporal outbreak patterns. Recent research has demonstrated that mosquito abundance is causally related to WNv/SLE prevalence, providing a practical starting point for developing mosquito-borne disease forecasting systems. When focusing on building mosquito population models, understanding the reproduction environment of Culex mosquitos (WNv and SLE's primary vectors) is key: they rely on warmth, water, and vegetation to reproduce. Previous work has shown that global-coverage multispectral imagery (MSI) (i.e., Landsat 8, Sentinel-2) is a valuable resource for characterizing vegetation health as a predictor of mosquito population, but it is limited in that it may not provide the spatial resolution necessary to distinguish between, e.g., a well-fertilized lawn (poor Culex habitat) and a stand of trees (good Culex habitat). The backscatter information collected by synthetic aperture radar (SAR) imagery provides opportunity to distinguish between broader categories of vegetation type, potentially helping to fill this gap. This research uses publicly available global-coverage MSI and SAR imagery (Landsat 8, Sentinel-2, and Sentinel-1) to explore if vegetation type, in tandem with vegetation health, improves our ability to forecast mosquito populations. vegetation characterization is done over the Greater Toronto Area from 2014 to 2017, and we derive weekly time series from MSI, spectral indices, and SAR for this time period. We then quantify the strength of vegetation health and type as a predictor of Culex abundance.

关键词： multispectral imagery synthetic aperture radar random forest NDvI

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Applications of Machine Learning algorithms via Google Earth Engine Interface to Interpret Snowline Altitudes: A Case Study in Chandra Basin 3rd

Applications of Machine Learning Algorithms via Google Earth...

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3rd International conference on River Corridor Research and Management, RCRM 2023

作者： Gaddam, vinay Kumar Ele, Sree Lakshmi Bhandari, Soniya Raavi, Sindhura Kulkarni, Anil v. Ranjan, Rakesh Department of Civil Engineering V.R. Siddhartha Engineering College Vijayawada India Department of Management CEP Indian Institute of Technology Patna Patna India Department of Environmental Science Central University of Himachal Pradesh Himachal Pradesh India Acharya Nagarjuna University Guntur India Divecha Center for Climate Change Indian Institute of Science Bangalore India DST Center of Excellence Department of Geology Sikkim University Gangtok India

ISBN: (纸本)9789819712267

Cryospheric components are sensitive to changes in climate and monitoring them is a challenging task in the rugged topography of the Himalaya. Many studies were carried out to assess the changes in Cryosphere like glacial retreat, mass balance, changes in runoff and weathering effect in glaciated valleys. But very few studies have been carried out on the assessment of Snow Line Altitude (SLA), which is an important parameter to understand the sensitivity of glaciers to climate change. Mapping and monitoring the SLAs of glaciers using in situ methods is a crucial task since it is a zonal feature and has high spatio-temporal variability. Remote sensing-based feature tracking is the alternative way for mapping the snowline altitudes of glaciers. With the recent development of various machine learning algorithms, segregation of identical features on the multispectral imagery and tracking the position of SLA’s has become easy. Few of them are Otsu, K-means, cascade K-means, random forest, minimum distance, smile cart and support vector machine algorithms. Thus, this study aims to understand the applicability of machine learning algorithms to map and analyse the variations in SLAs of glaciers in the Chandra River basin, a tributary of the Sutlej River. During the analyses, SLAs are mapped and snowline altitudes are extracted. It indicates that the SLAs of glaciers vary from 4100 to almost 5260 ± 30 m. All the glaciers are completely covered with seasonal snow during the accumulation period starting from November to April, and then snowline starts retreating from the lower altitude i.e., 3800 m.a.s.l, and reaches a maximum altitude by the end of the ablation period (in this study it is 5259 ± 30 m.a.s.l). Analyses and observations suggest that all the applied methods performed similarly except the support vector machine and minimum distance methods. Estimated uncertainties be within ± 30 m. The regional SLA is observed higher during the hydrological year 2003–04 (estimated

关键词： Support vector machines

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Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, Landsat 9 TIRS, and PlanetScope imagery with Machine Learning and Data Fusion

Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, L...

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2024 International conference on Computer and Applications, ICCA 2024

作者： Bah, Souleymane Houegbe, M.D. Tanguy Mesreil Megnigbeto, Emmanuel Wilfried G. Houndji, vinasetan Ratheil Kouton, Bignon Nicanor Gouwakinnou, N. Gérard Ezin, C. Eugène Institut de Mathématiques et de Sciences Physiques Université d'Abomey-Calavi Dangbo Benin Laboratoire d'Ecologie de Botanique et de Biologie végétate BiPLAC Parakou Benin Institut de Recherche sur le Coton Division protection phytosanitaire Parakou Benin Institut de Formation et de Recherche en Informatique Université d'Abomey-Calavi Abomey-Calavi Benin

ISBN: (纸本)9798350367560

The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signal processing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support vector Machine (SvM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signal processing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75% and a Kappa index of 86.36% with CatBoost algorithm. © 2024 IEEE.

关键词： Cotton

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Target Detection in Airborne hyperspectral imagery and its Sensitivity to Different Atmospheric Correction Methods

Target Detection in Airborne Hyperspectral Imagery and its S...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International conference on

作者： C.v.S.S Manohar Kumar Sudhanshu Shekhar Jha Rama Rao Nidamanuri Department of Earth and Space Sciences Indian Institute of Space Science and Technology Thiruvananthapuram India Leipzig Institute for Meteorology Leipzig University Leipzig Germany

Fine resolution hyperspectral imagery has promising application in the detection of natural and synthesized objects. Atmospheric correction (AC) is an important part of the pre-processing chain involved in target detection from remote sensing imagery. Different AC model approximates the atmosphere using different mathematical assumptions, resulting in different quality of the surface reflectance obtained. However, the effects of the choice of AC models on the overall application of hyperspectral imagery for target identification is unaddressed. We designed an experimental setup at Gudalur town, Tamil Nadu, India to study the effects of AC models on the performance of target identification using airborne hyperspectral imagery. State-of the-art spectral unmixing and target detection algorithms are applied to hyperspectral imagery for performance assessment. Results indicate a substantial aberration of target detection performance as a function of the atmospheric correction model.

关键词： Reflectivity Sensitivity Atmospheric modeling Computational modeling Object detection Mathematical models Libraries

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Fusing Conventional and Deep Learning Features for hyperspectral Image Change Detection

Fusing Conventional and Deep Learning Features for Hyperspec...

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2022 IEEE International conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Bobate, Nikhi Yadav, Palla Parasuram Narasimhadhan, A.v. Nit Surathkal Electronics and Comm. Dept. Karnataka India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

In the field of remote sensing technology Change Detection (CD) is one of the major areas of research. Changes that have occurred on the earth's surface over time can be detected with this tool. hyperspectral Image (HSI) data with high spectral resolution can help in identifying subtle changes than the typical multispectral image (MSI), and CD technology has benefitted immensely with the applications of HSI. Traditional CD techniques that used MSI as their input data are challenging to implement on HSI due to the high dimensionality of hyperspectral data. Furthermore, HSI data is affected by a lot of distortion and redundancy, contaminating the spectral-only information for CD purposes. CD accuracy can be improved by extracting the useful features of HSI. In Change Detection algorithms, the initial step is to extract features. Traditionally it is done using arithmetic operation, image transformation, and statistical methods. While some advanced strategies for extracting features are utilizing convolutional neural networks (CNNs) using the deep learning method. In this work, we aimed to integrate the conventional features with CNN extracted features to boost the overall ac-curacy of popular DL-based CD techniques. Spectral matching algorithms are used for extracting conventional features. In addition, appropriate changes are made to the recent deep learning architectures called Three-Directions Spectral-Spatial Convolution neural network (TDSSC) and General End-To-End Neural Network (GETNET), to fuse the conventional features. Farmland, River and USA data sets are used for experimentation. The proposed approach proves to be useful in improving the performance of DL-based CD techniques. © 2022 IEEE.

关键词： Change detection

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