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

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Shrinkage estimators for covariance matrices in spectral remote sensing 30

Shrinkage estimators for covariance matrices in spectral rem...

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

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

ISBN: (纸本)9781510673816;9781510673809

This talk describes investigations of shrinkage parameter estimates for covariance matrices used in spectral processing of remote sensing imagery, such as for target, anomaly, or change detection. These estimates are derived in the context of cross-validated fiting of Gaussian likelihood models to the non-target background distribution. Here, the utility of these estimates is evaluated for Gaussian and non-Gaussian distributions. An alternative criterion, based on matched-filter detection of "generic" targets, is derived and compared to the estimated likelihood criterion as a way to choose the shrinkage parameter.

关键词： Covariance matrix Cross Validation Regularization Shrinkage Maximum Likelihood hyperspectral imagery Background estimation Target detection

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Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques 30

Chemical signature characterization with hyperspectral image...

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

作者： Byler, Eleanor Forland, Brenda McKay, Myles Pacific Northwest Natl Lab Seattle WA 98109 USA Univ Washington Seattle WA USA

ISBN: (纸本)9781510673816;9781510673809

The high spectral resolution afforded by hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use/land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

关键词： hyperspectral Imaging HSI Deep Learning Artificial Intelligence Data Augmentation

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Multivariate methods to explore system sensitivities for hyperspectral subpixel target detection 30

Multivariate methods to explore system sensitivities for hyp...

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

作者： Canas, Chase Kerekes, John P. Brown, Scott D. Rochester Inst Technol Ctr Imaging Sci 1 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510673816;9781510673809

To explore system sensitivities in hyperspectral subpixel target detection, multivariate methods are applied to output detection metrics generated from a statistical target detection model. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) statistical model generates probabilities of detection (PD) and false alarm (PFA) using spectral libraries of target and background materials. This allows for the computation of the area under the receiver operating characteristic curve (AUC). To explore sensitivities within elements (e.g. scene, atmosphere, sensor) of the remote sensing system, ensembles of model-based scenarios are generated using combinations of the aerosol visibility, solar angle, and sensor viewing angle. Output detection metrics (PD, AUC) from these scenarios were cached into a high-dimensional tensor, before utilizing multivariate methods (e.g. interpolation and regression) to explore sensitivities and correlations between system variables and detection. Inferences on limitations of detection within the system are drawn from multivariate contour regions which characterize joint parametric parameters required to exceed a desired threshold of detection. The outlined methods aim to provide an initial framework to investigate both specific and generalizable limitations of detection across various scenes (e.g. rural, urban, maritime, and desert), environmental conditions (e.g. solar angle, haze, clouds), sensor characteristics (e.g. noise, viewing angle) and processing configurations (e.g. feature selection, detector algorithm).

关键词： spectral imagery remote sensing rare target detection system limitations novel targets multivariable analysis statistical modeling

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hyperspectral-multispectral image fusion using nearest-neighbor diffusion-based sharpening algorithm 28

Hyperspectral-multispectral image fusion using nearest-neigh...

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

作者： Ducay, Rey Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (纸本)9781510650657;9781510650640

The nearest-neighbor diffusion-based algorithm (NNDiffuse) has seen great success in multispectral pansharpening. Here, we extend the capabilities of NNDiffuse to perform image fusion of high-res multispectral and low-res hyperspectral images (HRMSI+LRHSI fusion). Unlike learning-based frameworks which are computationally expensive and require extensive optimization and/or training time, NNDiffuse is fast, radiometrically accurate (introduces less spectral distortions compared to other state-of-the-art methods), and requires no training data. We introduce the utility of NNDiffuse in hyperspectral-panchromatic and hyperspectral-multispectral sharpening and look at workflows dealing with low-res HSI bands that fall outside the HRMSI spectral response functions. Sharpened image quality is assessed using image-wide metrics. Sharpening performance is also measured on the fused images in terms of their utility in pixel classification and ACE target-background separability. NNDiffuse and DL- and non DL-based methods are presented using hyperspectral imagery from SHARE2012, AVIRIS-NextGen, Hyperspec-VNIR-C, and ROSIS sensors.

关键词： hyperspectral imagery panchromatic sharpening ACE target detection

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A Comparison of Dimensionality Reduction Techniques for hyperspectral imagery 28

A Comparison of Dimensionality Reduction Techniques for Hype...

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

作者： Race, Benjamin Wittman, Todd The Citadel Dept Math Sci 171 Moultrie St Charleston SC 29409 USA

ISBN: (纸本)9781510650657;9781510650640

Due to the high-dimensional nature of hyperspectral images, dimensionality reduction techniques can improve image analysis by reducing memory requirements, making image processing techniques more efficient, and sometimes increasing the accuracy of the results. A wide variety of dimensionality reduction (DR) algorithms have been proposed, ranging from linear transformations like Principal Component Analysis (PCA) to more computationally intensive manifold learning algorithms such as Locally Linear Embedding ( LLE). The best dimensionality reduction technique and the ideal dimensionality depend on the dataset and the image processing task under consideration. This paper compares various dimensionality reduction methods by evaluating the performance of image analysis tasks on available hyperspectral datasets with ground truth reference.

关键词： hyperspectral images dimensionality reduction anomaly detection classification

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Partially Supervised Detection in hyperspectral imagery 28

Partially Supervised Detection in Hyperspectral Imagery

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

作者： Heinz, Daniel C. Bahr, Thomas Streutker, David Terrie, Greg Ingram, Michael Harris Geospatial Solut Inc Herndon VA 20170 USA Harris Geospatial Solut GmbH D-82205 Gilching Germany

ISBN: (纸本)9781510650657;9781510650640

In this paper, performance of state-of-the-art partially supervised detection algorithms are compared. It can be difficult to characterize the performance of detection algorithms using field data, especially at the subpixel level, due to limited ground truth. Fortunately, the SpecTIR hyperspectral Airborne Experiment (SHARE) 2012 contains multiple sets of targets for testing detection algorithms with excellent ground truth. In this paper we utilize field spectra acquired for six targets to evaluate the performance of multiple detection algorithms. Each method is initialized with a single field spectra target signature, and detection performance of each method is separately assessed for each of the six targets. Detailed evaluation of these detection methods on the SHARE 2012 hyperspectral data is provided.

关键词： UnConstrained Abundance (UCA) Nonnegatively Constrained Abundance (NCA) Fully Constrained Abundance (FCA) Constrained Energy Minimization (CEM) Matched Filter (MF) Mixture Tuned Matched Filter (MTMF) Adaptive Cosine/Coherence Estimator (ACE) Spectral Angle Mapper (SAM) SpecTIR hyperspectral Airborne Experiment (SHARE) 2012

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Improvements in Multi- and hyperspectral imagery Standards: The Spectral NITF Implementation Profile 28

Improvements in Multi- and Hyperspectral Imagery Standards: ...

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

作者： Eckstein, Barbara A. Smith, Jason S. Durdall, Jennifer P. L3Harris Technol 2235 Monroe St Herndon VA 20171 USA L3Harris Technol 400 Initiat Dr Rochester NY 14624 USA Natl Geospatial Intelligence Agcy Res Directorate 7500 GEOINT Dr Springfield VA 22150 USA

ISBN: (纸本)9781510650657;9781510650640

Data standards enable low-cost integration and interoperability for data hosted by an enterprise managing multiple data sources, yet standards must change to keep pace with technological advances. hyperspectral imagery (HSI) systems have evolved beyond the 2011 tactical, i.e., airborne, HSI standard for systems transmitting imagery using the National imagery Transmission Format (NITF) and hosted within the National System for Geospatial Intelligence (NSG). The Research Directorate of the National Geospatial-Intelligence Agency (NGA) created the Spectral NITF Implementation Profile (SNIP) to realize HSI systems' advanced capabilities, incorporating improvements in remote sensing made over the past decade. Ratified by U.S. government standards committees, the SNIP is a mandatory Department of Defense and Intelligence Community standard that applies to any spectroradiometric system regardless of the number of bands. The SNIP supports spectroradiometric exploitation, geopositioning and mensuration, search and discovery, and dissemination controls. SNIP improvements include support for air, space, and ground-based platforms;new geopositioning extensions that provide more complete and accurate results;and per-pixel metadata that include spectral smile, noise equivalent spectral radiance, and keystone. SNIP reference implementation products (RIPs), that is, datasets encoded to the SNIP standard, are derived from a U.S. National Aeronautics and Space Administration (NASA) Hyperion dataset. The publicly released RIPs support interoperability tests and algorithm development. During development, the RIPs test dataset design, sometimes revealing issues that can be resolved with a revised design. This paper describes the SNIP, including development premises, design, collaboration efforts, history, and plans. It also describes the RIPs and includes a short overview of the NITF standard.

关键词： HSI hyperspectral standard MSI multispectral standard National imagery Transmission Format NITF SNIP Spectral NITF Implementation Profile

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Exploring Crop Yield Prediction with Remote Sensing imagery and AI 3

Exploring Crop Yield Prediction with Remote Sensing Imagery ...

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International conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Kavipriya, J. Vadivu, G. SRM Inst Sci & Technol Data Sci & Business Syst Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Planning for agriculture, allocating resources, and ensuring food security all depend on the accurate and timely estimate of the crop yield. This paper investigates the use of Machine Learning (ML) and Deep Learning methods (DL) to forecast crop yield using remote sensing images. multispectral and hyperspectral images from remote sensing provide a useful source of data for tracking crop health and estimating prospective yield. This study scrutinizes the advantage of the abundant spectral and spatial data that remote sensing sensors have collected. Convolutional neural networks (CNNs) and Machine learning are utilized for feature extraction and prediction, leveraging the hierarchical representations acquired from remote sensing data. A dataset of ground-truth yield data and historical satellite photos are used to train the DL and ML algorithms. In order to improve the model's generalization to various geographic locations and cropping seasons, transfer learning techniques can also be used. Moreover, interpretable deep learning methods and attention mechanisms are investigated to improve the model's transparency and reveal key elements that influence yield prediction. By giving farmers, decision-makers, and other stakeholders timely and precise information on crop output estimates, the research's findings have the potential to greatly advance precision agriculture methods. Deep learning methods combined with satellite imagery show approximately 90% accuracy for scalable and effective agricultural production monitoring, which will ultimately help sustainable farming methods.

关键词： Remote sensing Yield Prediction Artificial Intelligence Machine Learning Deep Learning

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EVALUATION OF DEEP LEARNING SEMANTIC SEGMENTATION FOR LAND COVER MAPPING ON multispectral, hyperspectral AND HIGH SPATIAL AERIAL imagery 44

EVALUATION OF DEEP LEARNING SEMANTIC SEGMENTATION FOR LAND C...

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44th Asian conference on Remote Sensing, ACRS 2023

作者： Panuntun, Ilham Adi Chen, Ying-Nong Jamaluddin, Ilham Tran, Thi Linh Chi Center for Space and Remote Sensing Research National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City320317 Taiwan Department of Computer Science and Information Engineering National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City320317 Taiwan

ISBN: (纸本)9781713893646

In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application. © 2023 ACRS. All Rights Reserved.

关键词： deep learning hyperspectral image land cover multi-spectral image semantic segmentation

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An Effective Transfer Learning Based Landmark Detection Framework for UAV-Based Aerial imagery of Urban Landscapes

An Effective Transfer Learning Based Landmark Detection Fram...

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IEEE SoutheastCon conference

作者： Praveen, Bishwas Menon, Vineetha Mukherjee, Tathagata Mesmer, Bryan Gholston, Sampson Corns, Steven Univ Alabama Dept Comp Sci Huntsville AL 35899 USA Univ Alabama Dept Ind & Syst Engn & Management Huntsville AL USA Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO USA

ISBN: (纸本)9781665476119

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured through UAVs. This study proposes an efficient transfer learning method for feature extraction using a ResNet50 architecture, paired with a FasterRCNN object detection for an automated landmark detection framework. Additionally, a novel technique for hierarchical image annotation and synthetic sampling is also introduced to address the issue of class imbalance. Empirical results prove that our proposed approach outperforms other state-of-the-art landmark detection methodologies compared.

关键词： landmark detection transfer learning ResNet50 FasterRCNN deep learning aerial imagery

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