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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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Water Wave Glint Corrections, Water Depth, Light Attenuation and WorldView-3 Remote Sensing algorithms for Indian River Lagoon

Water Wave Glint Corrections, Water Depth, Light Attenuation...

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conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions held at SPIE Remote Sensing conference

作者： Bostater, Charles R., Jr. Aziz, Samin Florida Inst Technol Marine Environm Opt Lab Melbourne FL 32901 USA Florida Inst Technol Ctr Remote Sensing Dept Ocean Engn & Marine Sci Melbourne FL 32901 USA

ISBN: (数字)9781510630048

ISBN: (纸本)9781510630031;9781510630048

A water wave glint correction algorithm, light attenuation depths and water depths estimated from satellite imagery in the Indian River Lagoon and Banana River estuarine waters provide a means to evaluate light penetration or water clarity. The shallow water estuarine study area is along Florida's east coast area known as"space coast" Florida. In-situ data, including water depths obtained from bathymetric charts, in-situ hyperspectral attenuation signatures and satellite-based reflectance factors are obtained from atmospherically corrected visible and near infra-red channels of World View-3 satellite imagery. The high spatial, radiometric and 11bit digital resolution satellite imagery is used to study water visibility that is an important ecosystem quality indicator. In shallow coastal waters as well as deeper water areas and nearby coastal waterways, the effects of water surface gravity wave facets and sun glint influence the estimates of water quality related variables estimated at the pixel level. The results have significant implications related to the estimation of the depth of penetration of light using satellite-based methods in coastal waters. Application of atmospherically corrected satellite imagery followed by glint removal is shown using translating and dilating derivative wavelet algorithms to estimate the attenuation depth of light in water. The research has been conducted in order that results may be transferable to other estuaries that suffer from reduction in light penetration due to factors such as turbidity or harmful algal blooms.

关键词： water visibility hyperspectral multispectral spectral attenuation water quality radiative transfer Monte Carlo simulations satellite imagery glint corrections wavelet algorithms derivative spectroscopy atmospheric correction wave facet glint removal

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Comparison of Bad Pixel Replacement Techniques for LWIR hyperspectral imagery 24

Comparison of Bad Pixel Replacement Techniques for LWIR Hype...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Martin, Jacob A. Islas, Genesis RYMT Air Force Res Lab Wright Patterson AFB OH 45433 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

This paper compares four different bad pixel replacement algorithms for LWIR hyperspectral imagery representing both physics-based unmixing and statistics-based methods. Testing is performed on a measured dataset using detection performance as a comparison metric and synthetic dataset with reconstruction error and detection performance used to compare. It is found that a statistics-based covariance matched filter method generally performs best of the four methods tested but at significantly greater computational cost. A simple plate metaphor interpolation is the fastest technique but struggles to correct pixels where sharp spectral or spatial difference are present. Meanwhile, an endmember-based unmixing approach provided a balance between the two in terms of computational complextity and reconstruction performance.

关键词： hyperspectral imaging Long wavelength infrared Detection and tracking algorithms Sensors Target detection hyperspectral target detection Reconstruction algorithms

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Underwater target detection with hyperspectral imagery for search and rescue missions 24

Underwater target detection with hyperspectral imagery for s...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Eken, Isa Cem Cetin, Yasemin Yardimci Middle East Tech Univ Dumlupinar Blvd 1 TR-06800 Cankaya Turkey

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Fast and precise target detection is an issue in underwater object detection applications such as search and rescue missions. hyperspectral imaging could be effective in determining different elements in a region thanks to its ability to provide detailed spectral information. Utilization of this rich spectral information calls for the analysis of spectral differences of objects on water surface (dry/wet) and underwater conditions. In our study, we investigated the effect of water column to the target spectrum with two water column correction models and evaluated the results obtained with these models in two detection algorithms. The results show that the methodology enhances underwater object detection performance. The materials used for the study are selected to be typical in search and rescue missions such as metal, cotton fabric with different colors and blue denim to evaluate the effect of these cases on the detectability of targets for such scenarios.

关键词： hyperspectral Underwater Target Detection Search and Rescue

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Seeing through heavily polluted satellite imagery using QUAC 24

Seeing through heavily polluted satellite imagery using QUAC

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Adler-Golden, Steven Bernstein, Lawrence St Peter, Benjamin Tannian, Bridget Spectral Sci Inc Burlington MA 01803 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Extremely thick haze caused by air pollution is observed in many satellite images of the earth, and in particular over eastern China Standard image display software typically provides satisfactory visualization of the ground through automated or user-driven scaling to enhance contrast;however, it does not perform well with these highly polluted scenes, where the haze is spatially non-uniform. Furthermore, estimation of surface reflectance using standard atmospheric correction software is highly problematic under these conditions due to very low visible transmission of the haze coupled with lack of knowledge of its optical properties, which may not conform to the haze or aerosol models in the software. In this paper we show that a version of the empirical Quick Atmospheric Correction (QUAC) algorithm, adapted for spatially dependent scattering, produces visually satisfying imagery of the entire ground in multispectral satellite scenes containing thick haze, and that the output reflectance spectra appear to be realistic enough for performing basic surface classification. The QUAC algorithm is applicable to multispectral and hyperspectral imagery with any number of wavelength bands, including true color (RGB) imagery, and does not require radiometrically calibrated data.

关键词： atmospheric correction multispectral image haze air pollution Planet Labs QUAC

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Onboard Cubesat Data Processing for hyperspectral Detection of Chemical Plumes 24

Onboard Cubesat Data Processing for Hyperspectral Detection ...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Theiler, James Foy, Bernard R. Safi, Claira Love, Steven P. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

We describe the development and implementation of plume detection algorithms under severe bandwidth and processing constraints imposed by a CubeSat architecture. In particular, two ideas will be presented: one employs onboard processing to reduce the data that is downlinked, and one employs the Sparse Matrix Transform (SMT) to speed up the onboard computation of an approximate Mahalanobis distance.

关键词： cubesat satellite hyperspectral imagery algorithm plume detection sparse matrix transform Mahalanobis distance matched filter adaptive coherence estimator

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A comparison of GEOBIA Vs PBIA machine learning methods for lithological mapping using Sentinel 2 imagery: Case study of Skhour Rehamna, Morocco

A comparison of GEOBIA Vs PBIA machine learning methods for ...

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Moroccan Geomatics (Morgeo), 2020 IEEE International conference of

作者： Serbouti Imane Raji Mohamed Hakdaoui Mustapha Dept. of Geology Hassan II University of Casablanca Casablanca Morocco Dept. of Geology Faculty of Sciences Ben M’Sik Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9781728158068

ISBN: (纸本)9781728158075

An extensive range of remote sensing-based approaches has been used to lithological mapping in conjunction with multispectral satellite imagery. Thus, this study focuses on mapping and comparing thematic classification methods of lithologies using Sentinel 2 imagery in the southern of Skhour Rehamna. First, Pixel-Based Image analysis (PBIA) techniques were evaluated, particularly, RF and K-NN machine learning algorithms (MLAs), both methods generalized lithological maps that is characterized with “salt and pepper” noise of misclassified pixels and poorly defined boundaries. The second approach is Geographic ObjectBased Image Analysis (GEOBIA), which involved multiresolution segmentation part based on several predefined scale, shape and compactness parameters that must be subjected to relative weighting in order segment the image to homogeneous areas, whilst, standard Nearest Neighbor classification was applied to the given samples. Then, classifications accuracies were assessed by comparing both methods using confusion matrix and Kappa coefficient. The results show that the GEOBIA approach provides the best mapping results with an overall accuracy of approximately 83.46% and Kappa coefficient of 0.76 compared with the results obtained by PBIA approach, with 81.92% and 80.79%, and Kappa coefficient of 0.72 and 0.72, for RF and K-NN respectively. As a conclusion, the results clearly indicate the potential of GEOBIA technique for lithological mapping.

关键词： lithological mapping Sentinel 2 imagery Skhour Rehamna MLAs PBIA GEOBIA MAPPING(MATHEMATICAL) mapping images imagery (Psychotherapy) salt-and-pepper noise cyhalothrin confusion matrix Morocco radiofrequency Classification Methods

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Generative adversarial networks for spectral super-resolution and bidirectional RGB-to-multispectral mapping

Generative adversarial networks for spectral super-resolutio...

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IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops

作者： Kin Gwn Lore Kishore K. Reddy Michael Giering Edgar A. Bernal United Technologies Research Center University of Rochester

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Acquisition of multi- and hyperspectral imagery imposes significant requirements on the hardware capabilities of the sensors involved. In order to keep costs manageable, and due to limitations in the sensing technology, tradeoffs between the spectral and the spatial resolution of hyperspectral images are usually made. Such tradeoffs are usually not necessary when considering acquisition of traditional RGB imagery. We investigate the use of statistical learning, and in particular, of conditional generative adversarial networks (cGANs) to estimate mappings from three-channel RGB to 31-band multispectral imagery. We demonstrate the application of the proposed approach to (i) RGB-to-multispectral image mapping, (ii) spectral super-resolution of image data, and (iii) recovery of RGB imagery from multispectral data.

关键词： image data hyperspectral imagery super-resolution MAPPING(MATHEMATICAL) red-green-blue colour model Statistical learning mapping tradeoffs Bidirectional imagery (Psychotherapy) images photomaps

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Dimensionality reduction for spatial-spectral target detection on hyperspectral imagery 24

Dimensionality reduction for spatial-spectral target detecti...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Kaufman, Jason R. Meola, Joseph Univ Dayton Res Inst 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab 2211 Avion Circle Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

hyperspectral images often contain hundreds of spectral bands. Man-made anti natural materials usually exhibit variability their reflective and emissive across these bands, which is exploitable via target detection algorithms. The high dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.

关键词： hyperspectral imagery dimensionality reduction spatial-spectral feature extraction spatial-spectral target detection spectral target detection

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Semi-supervised Deep Autoencoder Network for Graph-based Dimensionality Reduction of hyperspectral imagery 24

Semi-supervised Deep Autoencoder Network for Graph-based Dim...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Zhang, Xuewen Cahill, Nathan D. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Sch Math Sci Image Comp & Anal Lab Rochester NY 14623 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Nonlinear graph-based dimensionality reduction algorithms have been shown to be very effective at yielding low-dimensional representations of hyperspectral image data. However, the steps of graph construction and eigenvector computation often suffer from prohibitive computational and memory requirements. In the paper, we develop a semi-supervised deep autoencoder network (SSDAN) that is capable of generating mappings that approximate the embeddings computed by the nonlinear DR methods. The SSDAN is trained with only a small subset of the original data and enables an expert user to provide constraints that can bias data points from the same class towards being mapped closely together. Once the SSDAN is trained on a small subset of the data, it can be used to map the rest of the data to the lower dimensional space, without requiring complicated out-of-sample extension procedures that are often necessary in nonlinear DR methods. Experiments on publicly available hyperspectral imagery (Indian Pines and Salinas) demonstrate that SSDANs compute low-dimensional embeddings that yield good results when input to pixel-wise classification algorithms.

关键词： Deep learning Autoencoder Graph-based method Dimensionality reduction hyperspectral image

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Target Detection Using Artificial Neural Networks on LWIR hyperspectral imagery 24

Target Detection Using Artificial Neural Networks on LWIR Hy...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Martin, Jacob A. US Air Force Res Lab RYMT Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Results using a neural network classifier for LWIR HSI target detection are presented. Detection performance using two different single layer networks with 10 and 20 nodes in the hidden layer are presented. Additionally, a two-layer network with 20 nodes in the first layer and 10 in the second is also tested. The larger networks generally performbetter, as expected, but this is not necessarily true for all targets. Neural networks significantly outperform ACE across several detection metric for eight different targets. Even when ACE is applied to atmospherically-corrected emissivity datacubes, neural networks applied without atmospheric compensation still performbetter. Finally, it is found that incorporating atmospheric metadata into the network inputs can improve detection performance but is again target-dependent.

关键词： Target detection Neural networks hyperspectral imaging Long wavelength infrared Atmospheric sensing Network architectures Artificial neural networks Machine learning Sensors hyperspectral target detection

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