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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

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)9781510635623

ISBN: (纸本)9781510635623

Outbreaks of West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are projected to increase in frequency and intensity with climate change, underlining the need to develop better mosquito borne disease (MBD) forecasting systems. Spread by Culex, WNV and SLE have seemingly random spatial and temporal outbreaks, making such outbreaks difficult to predict. However, recent studies have found that mosquito abundance and WNV/SLE transmission are strongly correlated, providing researchers with a foundation for the development MBD forecasting systems. Mosquito populations are impacted by several environmental variables, such as humidity, temperature, vegetation, and available breeding habitat. Mosquito-population forecasting models are beginning incorporate spectral data, such as the normalized difference vegetation index (NDVI). Vegetation is a crucial habitat for some mosquito species, and spectral data offers the best estimate of this habitat virtually anywhere on Earth. Additionally, vegetation offers a proxy for understanding how water flows across a landscape, a crucial consideration in urban areas with high landscape heterogeneity. This research explores how the spatial scale (extent) of multispectral imagery used in mosquito population prediction models influences mosquito population forecasts, specifically in the Greater Toronto Area. We derive three monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2015;each time series is derived from images taken over the same locations, but using images taken over different spatial footprints. We then explore how spectral indices across the three spatial scales perform as predictors for combined Cx. restuans and Cx. pipiens populations.

关键词： multispectral imagery mosquito population forecasting spectral indices NDVI

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hyperspectral analysis: black inks separation in the Gough map of Great Britain 26

Hyperspectral analysis: black inks separation in the Gough m...

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conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Huang, Sihan Messinger, David W. Bove, Damien Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA Univ London London England

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The Gough Map is a late medieval map of the island of Great Britain, which has been receiving a lot of research attention and many studies have been conducted to analyze the map. With the hyperspectral image (HSI) of the Gough Map collected, we are allowed to further analysis the map in a different angle. Our goal is to semi-automatically identify and separate iron-gall and carbon black inks written on the Gough map. Unlike traditional target detection problems, there are unique characteristics in our data: Firstly, the targets are not sparsely distributed and their statistical contributions to the background estimation cannot be ignored. Secondly, the spectral differences between the targets are subtle. Lastly, the variances of the background data are so low that the distinction between it and noise is challenging. To address these issues, we made the following modification to the traditional adaptive coherence estimator (ACE): First, manually select the background data instead of using the whole data to estimate the background. Moreover, the pixels with strong spectral features of other pigments are removed and only the black ink candidates are feed into the detector. Thirdly, the number of eigenvectors were limited in the calculation of whitening operator so that the impact of noise can be controlled. It is clear that the trailing eigenvectors are usually dominated by the noise, so the majority of the information of background data can be effectively compressed only with the top-ranked eigenvectors. This is critical because whitening the noise means increasing the variance of the noise based on the fact that our background has super low variances. The whitening matrix needs to be used for whitening target candidates which have subtle spectral differences, and whitening the noise will cause the spectral features to disappear in the whitening sub-space. In conclusion, the whitening operator used in ACE should whiten the useful information of background data and red

关键词： hyperspectral imagery ink identification target detection adaptive coherence estimator(ACE)

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Very High-Resolution imagery and Machine Learning for Detailed Mapping of Riparian Vegetation and Substrate Types

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REMOTE SENSING 2022年第4期14卷 954页

作者： Rommel, Edvinas Giese, Laura Fricke, Katharina Kathoefer, Frederik Heuner, Maike Moelter, Tina Deffert, Paul Asgari, Maryam Naethe, Paul Dzunic, Filip Rock, Gilles Bongartz, Jens Burkart, Andreas Quick, Ina Schroeder, Uwe Baschek, Bjoern German Fed Inst Hydrol Landscape Management Dept Vegetat Studies Am Mainzer Tor 1 D-56068 Koblenz Germany German Fed Inst Hydrol Dept Geodesy & Remote Sensing Am Mainzer Tor 1 D-56068 Koblenz Germany German Fed Inst Hydrol Dept Fluvial Morphol Sediment Dynam & Management Am Mainzer Tor 1 D-56068 Koblenz Germany GEOCOPTIX GmbH Max Planck Str 12 D-54296 Trier Germany Univ Appl Sci Koblenz Dept Math & Technol Joseph Rovan Allee 2 D-53424 Remagen Germany JB Hyperspectral Devices GmbH Am Botanischen Garten 33 D-40225 Dusseldorf Germany

Riparian zones fulfill diverse ecological and economic functions. Sustainable management requires detailed spatial information about vegetation and hydromorphological properties. In this study, we propose a machine learning classification workflow to map classes of the thematic levels Basic surface types (BA), Vegetation units (VE), Dominant stands (DO) and Substrate types (SU) based on multispectral imagery from an unmanned aerial system (UAS). A case study was carried out in Emmericher Ward on the river Rhine, Germany. The results showed that: (I) In terms of overall accuracy, classification results decreased with increasing detail of classes from BA (88.9%) and VE (88.4%) to DO (74.8%) or SU (62%), respectively. (II) The use of Support Vector Machines and Extreme Gradient Boost algorithms did not increase classification performance in comparison to Random Forest. (III) Based on probability maps, classification performance was lower in areas of shaded vegetation and in the transition zones. (iv) In order to cover larger areas, a gyrocopter can be used applying the same workflow and achieving comparable results as by UAS for thematic levels BA, VE and homogeneous classes covering larger areas. The generated classification maps are a valuable tool for ecologically integrated water management.

关键词： UAS airborne imagery remote sensing classification random forest OBIA management riparian zones hydromorphology

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Analysis of spectral data using spatial context 25

Analysis of spectral data using spatial context

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Metcalf, Jeremy P. Olsen, Richard C. Naval Postgrad Sch 833 Dyer Rd Monterey CA 93943 USA

ISBN: (纸本)9781510626386

Spectral LiDAR analysis can be enabled by the use of spatial context, spatial structure, and prior information in the form of map data. LiDAR intensity imagery are analyzed here using an object-based approach which segments the data according to vector information obtained from OpenStreetMap and other vector map information. Polygons and features from the map vectors are used to establish regions of interest for analysis. This automates the training process for use of traditional statistical classifiers and machine learning algorithms. Map-derived objects can demonstrate multiple spectral components which must be resolved to define the primary object, and its semantic label.

关键词： multispectral imagery image segmentation feature extraction Optech Titan GEOBIA LiDAR intensity

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Algorithm development with on-board and ground-based components for hyperspectral gas detection from small satellites 25

Algorithm development with on-board and ground-based compone...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

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

ISBN: (纸本)9781510626386

Los Alamos is currently working toward demonstrating Cubesat-based hyperspectral detection of gas-phase chemical plumes, a goal initially pursued in the internally funded Targeted Atmospheric Chemistry Observations from Space (TACOS) project, and now advancing toward space deployment with the NASA-funded Nanosat Atmospheric Chemistry hyperspectral Observation System (NACHOS). This paper will present a general overview of these projects. Bandwidth considerations prevent full datacube downloads, and so processing algorithms include an on-board processing component to provide matched-filter and RX images for the gases of interest. The downlinked data will additionally include the full spectrum for a small sample of pixels, and one of the challenges for ground-based analysis will be to incorporate these different but incomplete \views" of the datacube into a more physical interpretation/analysis of the downlinked data.

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

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Multi-sensor anomalous change detection at scale 25

Multi-sensor anomalous change detection at scale

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Ziemann, Amanda Ren, Christopher X. Theiler, James Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA

ISBN: (纸本)9781510626386

Combining multiple satellite remote sensing sources provides a far richer, more frequent view of the earth than that of any single source;the challenge is in distilling these petabytes of heterogeneous sensor imagery into meaningful characterizations of the imaged areas. To meet this challenge requires effective algorithms for combining heterogeneous data to identify subtle but important changes among the intrinsic data variation. The major obstacle to using heterogeneous satellite data to monitor anomalous changes across time is this: subtle but real changes on the ground can be overwhelmed by artifacts that are simply due to the change in modality. Here, we implement a joint-distribution framework for anomalous change detection that can effectively "normalize" for these changes in modality, and does not require any phenomenological resampling of the pixel signal. This flexibility enables the use of satellite imagery from different sensor platforms and modalities. We use multi-year construction of the Los Angeles Stadium at Hollywood Park (in Inglewood, CA) as our testbed, and exploit synthetic aperture radar (SAR) imagery from Sentinel-1 and multispectral imagery from both Sentinel-2 and Landsat 8. We explore results for anomalous change detection between Sentinel-2 and Landsat 8 over time, and also show results for anomalous change detection between Sentinel-1 SAR imagery and Sentinel-2 multispectral imagery.

关键词： change detection anomalous change detection multispectral imagery synthetic aperture radar multi-sensor multi-modal

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Applications of Spectral Image Quality Equation for Longwave Infrared hyperspectral imagery 25

Applications of Spectral Image Quality Equation for Longwave...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Rankin, Blake M. Broadwater, Joshua B. Jansing, E. David Johns Hopkins Univ Appl Phys Lab Johns Hopkins Rd Laurel MD 20723 USA

ISBN: (纸本)9781510626386

hyperspectral imaging (HSI) technologies span the electro-optical and infrared domains. Longwave infrared (LWIR) HSI is particularly well suited for chemical and material identification in both day and night conditions due to the fact that longwave signals depend on thermal emission and material composition. However, exploitation performance is impacted by spectral data quality, which is driven by fundamental sensor noise characteristics, focal plane array health, spectral and radiometric calibration accuracy, and weather conditions. Previous algorithms have focused on quantifying spectral quality in the visible, near infrared, and shortwave infrared domains. More recently, we developed a spectral image quality equation (SIQE) based on Bayesian Information Criterion (BIC) for quantifying spectral quality of LWIR HSI data. Here, we further develop the algorithm to provide a more intuitive interpretation of the resulting BIC scores by transforming the scores into a metric that more closely resembles target detection scores. In addition to showing how SIQE is correlated with noise-equivalent spectral radiance, we illustrate several applications of SIQE, including the impact of atmospheric/environmental interferences and calibration errors. Our results reveal that SIQE is an effective metric for quantifying hyperspectral data quality, and thus, can be used for filtering data cubes prior to implementing exploitation algorithms.

关键词： hyperspectral imaging longwave infrared spectral image quality

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Object Detection and Classification in Aerial hyperspectral imagery using a Multivariate Hit-or-Miss Transform 25

Object Detection and Classification in Aerial Hyperspectral ...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Macfarlane, Fraser Murray, Paul Marshall, Stephen White, Henry Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland BAE Syst Air Sect Bristol Avon England

ISBN: (纸本)9781510626386

High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques.

关键词： hyperspectral image processing Mathematical morphology Hit-or-Miss Transform Template matching Object detection

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algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXV

Algorithms, Technologies, and Applications for Multispectral...

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algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXV 2019

ISBN: (纸本)9781510626379

The proceedings contain 57 papers. The topics discussed include: unsupervised hyperspectral band selection in the compressive sensing domain;simplified measurement of point spread functions of hyperspectral cameras for assessment of spatial coregistration;solar-induced fluorescence retrievals in the context of physiological, environmental, and hardware-based sources of uncertainty;stray light characterization in a high-resolution imaging spectrometer designed for solar-induced fluorescence;SAGE iv pathfinder multi-spectral imaging spectrometer telescope paves the way for semi-custom CubeSat imaging missions;extended characterization of multispectral resolving filter-on-chip snapshot-mosaic CMOS cameras;advances in active infrared spectroscopy for trace chemical detection;and active LWIR hyperspectral imaging and algorithms for rapid standoff trace chemical identification.

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Semi-Supervised Discriminant Feature Selection for hyperspectral imagery Classification 25

Semi-Supervised Discriminant Feature Selection for Hyperspec...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Dong, Chunhua Naghedolfeizi, Masoud Aberra, Dawit Zeng, Xiangyan Ft Valley State Univ Dept Math & Comp Sci 1005 State Univ Dr Ft Valley GA 31030 USA

ISBN: (纸本)9781510626386

Sparse representation classification (SRC) is being widely applied for target detection in hyperspectral images (HSI). However, due to the problem of the curse of dimensionality and redundant information in HSI, SRC methods fail to achieve high classification performance via a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is a challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature selection (DFS) method for hyperspectral image classification in the eigenspace. Firstly, our proposed DFS method selects a subset of discriminant features by solving the combination of spectral and spatial hypergraph Laplacian quadratic problem, which can preserve the intrinsic structure of the unlabeled pixels as well as both the inter-class and intra-class constraints defined on the labeled pixels in the projected low-dimensional eigenspace. Then, in order to further improve the classification performance of SRC, we exploit the well-known simultaneous orthogonal matching pursuit (SOMP) algorithm to obtain the sparse representation of the pixels by incorporating the interpixel correlation within the classical OMP by assuming that neighboring pixels usually consist of similar materials. Finally, the recovered sparse errors are directly used for determining the label of the pixels. The extracted discriminant features are compatibly used in conjunction with the established SRC methods, and can significantly improve their performance for HSI classification. Experiments conducted with the hyperspectral data sets and different experimental settings show that our proposed method increases the classification accuracy and outperforms the state-of-the-art feature selection and classification methods.

关键词： Sparse Representation Image Classification Dimensionality Reduction hyperspectral imagery Feature Selection Laplacian Eigenspace Semi-Supervised Learning

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