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

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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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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Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery 40

Geological mapping in Morozumi range and Helliwell hills are...

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40th Asian conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

作者： Pour, Amin Beiranvand Park, Yongcheol Hong, Jong Kuk Muslim, Aidy M. Pradhan, Biswajeet Songdomirae-ro Yeonsu-gu Incheon21990 Korea Republic of Kuala Nerus Terengganu21030 Malaysia School of Systems Management and Leadership Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia

Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions. © 2020 40th Asian conference on Remote Sensing, ACRS 2019: "Progre

关键词： Landsat

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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 2

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

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2nd IEEE International conference of Moroccan Geomatics, MORGEO 2020

作者： Imane, Serbouti Mohamed, Raji Mustapha, Hakdaoui Hassan II University of Casablanca Dept. of Geology Faculty of Sciences Ben Mosik Hassan Casablanca Morocco Faculty of Sciences Ben m'Sik Hassan II University of Casablanca Dept. of Geology Faculty of Sciences Ben Mosik Hassan Casablanca Morocco

ISBN: (纸本)9781728158068

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. © 2020 IEEE.

关键词： Mapping

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Optimizing Deep Learning Model Selection for Angular Feature Extraction in Satellite imagery 25

Optimizing Deep Learning Model Selection for Angular Feature...

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

作者： Immel, Poppy Desai, Meera Moody, Daniela, I Ursa Space Syst New York NY 14850 USA Barnard Coll New York NY USA

ISBN: (纸本)9781510626386

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of Convolutional Neural Networks (CNNs) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be easily extended to similar problems, e.g. via transfer learning. Ursa Space Systems Inc. (Ursa) develops novel machine learning approaches to build custom solutions and extract answers from Synthetic Aperture Radar (SAR) satellite data fused with other remote sensing datasets. One application is identifying the orientation with respect to true north of the inlet pipe, which is one common feature located on the top of a cylindrical oil storage tank. In this paper, we propose a two-phase approach for determining this orientation: first an optimized CNN is used to probabilistically determine a coarse orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the location of the angular feature within 7.5 degrees. We present a systematic technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and dataset preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction algorithm from 86% to 94% and can be extended to similar applications.

关键词： Feature extraction Optimization (mathematics) Binary data Image classification Satellites Performance modeling Satellite imaging Remote sensing Convolutional neural networks

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