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

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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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Multiband remote 3D-ViT data fusion 30

Multiband remote 3D-ViT data fusion

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

作者： Kurz, Aidan G. Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673816;9781510673809

Satellites are equipped with diverse sensors, capable of capturing detailed information across a multitude of wavelengths. The fusion of multispectral data is pivotal to amplify the visual representation of the area of interest. The improvement of information representation allows for enhanced processing, analysis, and other crucial tasks for numerous fields of study, including remote sensing, defense, and material characterization. Previous solutions often utilize traditional signal processing techniques, including principal component analysis (PCA), to accomplish data fusion. By performing fusion on a feature level, extracted information about the area of interest texture and boundaries are combined. The introduction of neural network techniques improved the reconstruction of data similar to the results obtained by conventional inference of humans. For example, the use of deep learning algorithms in conjunction with PCA allowed for refined reduction of redundancy and distortion of spectral data, in comparison to traditional methods alone. The introduction of the Vision Transformer (ViT) architecture, originally developed for two-dimensional image data, has revolutionized image processing tasks, vastly improving performance at the cost of a large quantity of trainable parameters. Recent experimentation has proven that optimizing ViT for efficiency allows for comparable or even superior performance while lessening the computational cost. The transition from 2D to 3D information via utilization of additional depth and spatial data has also led to superior results as the added information allows for better representation of terrain features, making it invaluable for satellite imagery analysis. Combining the principles of ViT and 3D information to process complex satellite data can result in more effective data fusion to achieve a superior level of data visualization of multispectral satellite imagery in an efficient manner.

关键词： Data Fusion multispectral Fusion Remote Sensing Deep Learning

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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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Efficient end-to-end multispectral image compression 6

Efficient end-to-end multispectral image compression

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conference on Big Data VI - Learning, Analytics, and Applications

作者： Depoian, Arthur C., ii Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673915;9781510673908

multispectral imagery is instrumental across diverse domains, including remote sensing, environmental monitoring, agriculture, and healthcare, as it offers a treasure trove of data over various spectral bands, enabling profound insights into our environment. However, with the ever-expanding volume of multispectral data, the need for efficient compression methods is becoming increasingly critical. Enhanced compression not only conserves precious storage space, but also facilitates rapid data transmission and analysis, ensuring the accessibility of vital information. In particular, in applications such as satellite imaging, where bandwidth constraints and storage limitations are prevalent, superior compression techniques are essential to minimize costs and maximize resource utilization. Neural network-based compression methods are emerging as a solution to address this escalating challenge. While autoencoders have become a common neural network approach to image compression, they face limitations in generating customized quantization maps for training images, relying on feature extraction. However, the integration of bespoke quantization maps alongside feature extraction can elevate compression performance to levels previously considered unattainable. The concept of end-to-end image compression, encompassing both quantization maps and feature extraction, offers a comprehensive approach to represent an image in its simplest form. The proposed method considers not only the compression ratio and image quality but also the substantial computational costs associated with current approaches. Designed to capitalize on similarities within and across spectral channels, it ensures accurate reproduction of the original source information, promising a more efficient and effective solution for multispectral image compression.

关键词： multispectral Image Compression Remote Sensing hyperspectral Imaging Deep Learning/Machine Learning Artificial Intelligence Image Processing

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Comparative performance of machine learning algorithms for Forest Cover classification using ASI - PRISMA hyperspectral data

Comparative performance of machine learning algorithms for F...

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IEEE International Workshop on Metrology for Agriculture and Forestry (IEEE MetroAgriFor)

作者： Caputi, Eros Delogu, Gabriele Patriarca, Alessio Perretta, Miriam Gatti, Lorenzo Boccia, Lorenzo Ripa, Maria Nicolina Univ Tuscia Deim Viterbo Italy Univ Tuscia Dafne Viterbo Italy Univ Napoli Federico II Diarc Naples Italy

ISBN: (纸本)9798350312720

Remote sensing (RS) supports studies on land cover and land cover changes, agroforestry planning and helps in performing cross-checks in the framework of EU's common agricultural policy (CAP). multispectral data (with limited number of spectral bands) and multi-temporal processing (sequence of scenes captured on different periods of time) were mainly used for RS. The use of hyperspectral imagery such as those provided by the PRISMA satellite, could enable further advances. This study aims at evaluating and comparing classification methods of the PRISMA product. For this purpose, the ASI pre-processed radiance level data (L2D) was used to investigate an area in the Lazio region (Italy). All the classification approaches tested are advanced techniques based on machine learning. Experimentation was carried out using K-nearest neighbors (KNN), Support Vector Machine (SVM) and Random Forest (RF) algorithms, which were compared in terms of accuracy, number of ground control points (GCP) required and ease of implementation. The study showed that the Random Forest approach presents the best compromise among the three proposed approaches, also considering its applicability, which is related to the wider literature and established experimentation in the GIS environment. The research is one of the first studies with the purpose of using the PRISMA data and presents some methodologies for the operational use of this data.

关键词： PRISMA hyperspectral Random Forest K-Nearest Neighbor Support Vector Machine Tree Cover

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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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