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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Álvaro Moreno-Martínez Emma Izquierdo-Verdiguier Gustau Camps-Valls Marco Moneta Jordi Muñoz-Marí Nathaniel Robinson Jose E. Adsuara Manuel Campos Javier García-Haro Adrián Pérez Nicholas Clinton John Kimball Steven W. Running Numerical Terradynamic Simulation Group (NTSG) University of Montana USA Institute of Geomatics University of Natural Resources and Life Sciences (BOKU) Austria Image Processing Laboratory (IPL) Universitat de València Spain University of Montana USA UV-Environmental Remote Sensing Group (UV-ERS) Universitat de València Spain Google Inc. Mountain View CA USA

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

关键词： Earth remote sensing Artificial satellites MODIS Spatial resolution Uncertainty Vegetation mapping

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A Novel Parameter Estimation Method for Generalized Gamma Distribution Towards SAR Data processing

A Novel Parameter Estimation Method for Generalized Gamma Di...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Peng Zhang Beibei Li Mohamed El Yazid Boudaren Junkun Yan Ming Li Yan Wu National laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Engineering Ecole Militaire Polytechnique Bordj El Bahri Algiers Algeria Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Statistical modeling of synthetic aperture radar (SAR) data is a crucial step in SAR image processing. In this context, the generalized Gamma (GGamma) distribution, which generalizes many common distributions, has been recognized as a valid model. Parameter estimation remains, however, a challenging step that conditions the quality of model fitting to data and thus, of the required processing. In this paper, we propose a novel parameter estimation method for GGamma distribution in the log-domain, named as the maximum likelihood and logarithmic cumulants (ML-LC) method. The ML-LC method constructs a novel scale-independent shape parameter estimation in the log-domain based on the Mellin transform and maximum likelihood estimation, and proposes a feasible solution technique for the shape parameter based on the multistart local search (MLS), gradient descent (GD) and bisection methods. To assess the performance of our estimation method, we perform the goodness-of-fit test on SAR data. In addition, we apply the ML-LC method in some SAR data processing tasks covering image segmentation and classification. The results obtained confirm the interest of the proposed ML-LC method.

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Fusarium Wilt Inspection for Phalaenopsis Using Uniform Interval Hyperspectral Band Selection Techniques

Fusarium Wilt Inspection for Phalaenopsis Using Uniform Inte...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Bo-Han Chen Yen-Chieh Ouyang Mang Ou-Yang Horng-Yuh Guo Tsang-Sen Liu Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Chgein-I Chang Min-Shao Shih National Chung Hsing University Taichung Taiwan National Chiao-Tung University Hsinchu City Taiwan Taiwan Agriculture Research Institute Nan-Tou Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taichung Taiwan National Taipei University of Technology Taipei Taiwan Providence University Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

In this paper, we propose a method to inspect the quality of Phalaenopsis by using hyperspectral imaging techniques. Phalaenopsis is easy to get infected with Fusarium wilt. We use the k-means clustering method to find out that the reflection spectrum of Phalaenopsis stem changes. The methods of the Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) are then used to find the area of the infected area. The Harsanyi, Farrand and Chang (HFC) methods and virtual dimensions (VD) are used to estimate the amount of spectrum required for band selection (BS). Band priority (BP) is used to calculate the priority of each band, and band de-correlation (BD) will remove band data with high correlation with each other. Then use the support vector machine (SVM) to detect Phalaenopsis wilt. The detection accuracy of VNIR and SWIR is 0.81 and 0.86, respectively, with band selection.

关键词： Hyperspectral imaging Support vector machines Hybrid fiber coaxial cables Feature extraction Diseases Correlation Signal to noise ratio

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URBAN AREA IMPERVIOUS SURFACE ESTIMATION BY SUBPIXEL UNMIXING

URBAN AREA IMPERVIOUS SURFACE ESTIMATION BY SUBPIXEL UNMIXIN...

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IEEE International Geoscience and remote sensing Symposium

作者： Bai Xue Shuhan Chen Chia-Chen Liang Shengwei Zhong Peter F. Hu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore USA Zhejiang University China Department of Information Engineering Harbin Institute of Technology Harbin China Department of Anesthesiology University of Maryland School of Medicine Baltimore MD USA

Urban impervious surface area (ISA) is a key index toward urban eco-system and sustainable urban planning strategy. In this paper, a subpixel approach is proposed to estimate urban ISA values using linear spectral mixture analysis (LSMA)-based hyperspectral imaging techniques. In doing so the concept of virtual dimensionality (VD) is used to first estimate the number of endmembers, then an endmember finding approach is implemented to find VD-determined number of endmembers in a hyperspectral image. Finally, nonnegativity constrained least squares (NCLS) is performed for endmember unmixing. The hyperspectral image used in our approach provides a larger number of spectral dimensions than a multispectral image does so that a sufficient number of endmembers can be found from a hyperspectral image for ISA estimation. What is more, a relationship between ISA values and fractional endmember abundances can be further constructed by linear regression.

关键词： Hyperspectral imaging Training Estimation Linear regression Urban areas

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The Evaluation Of The Influence Of Land Surface Temperature In Energy Balance Estimated By S-Sebi Model

The Evaluation Of The Influence Of Land Surface Temperature ...

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remote sensing Conference (LAGIRS), IEEE Latin American GRSS & ISPRS

作者： N. S. Rocha P.S. Käfer D. Skokovic G. Veeck L. R. Diaz E. Kaiser C. M Carvalho B.K. Veettil S. T. L. Costa R. C. Cruz D. Robérti S. B. A. Rolim Laboratório de Sensoriamento Geológico (LabSRGeo) Postgraduate in Remote Sensing UFRGS (Federal University of Rio Grande do Sul) Porto Alegre (RS) Brazil Unidad de Cambio Global (UCG) Image Processing Laboratory (IPL) UV (University of Valencia) Valencia Spain Department of Physics UFSM (Federal University of Santa Maria) Santa Maria (RS) Brazil Laboratorio Interdisciplinar de Ciências Ambientais (LICA) UNIPAMPA (Federal University of Pampa) São Gabriel (RS) Brazil Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City Vietnam

ISBN: (数字)9781728143507

Evapotranspiration (ET) is one of the least understood components of the hydrological cycle. Its application is varied, from agricultural, ecological and hydrological monitoring, to control of the evolution of climate change. The goal of this work was to analyze the influence that uncertainties in the estimate of Land Surface Temperature (LST) can cause on ET estimates by S-SEBI model in Pampa Biome area. The results indicate that the daily evapotranspiration is higher when the pixel LST is lower, which also shows the influence of land use on the variability of ET. They also demonstrated the importance of LST's accuracy in the selection of the driest and wettest pixels in applying S-SEBI model, because when there are uncertainties in estimates of LST, the errors in the estimates of the energy components multiply. The Pampa Biome native grass crops have lower Latent Heat Flux (LET) than other land uses, with higher values of LET during the spring-summer period when compared to autumn-winter.

关键词： Land surface temperature Land surface Heating systems Biological system modeling remote sensing Soil Surface treatment

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Pampa Biome Environmental Particularities Regarding to Energy Balance

Pampa Biome Environmental Particularities Regarding to Energ...

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remote sensing Conference (LAGIRS), IEEE Latin American GRSS & ISPRS

作者： N. S. Rocha P.S. Käfer D. Skokovic G. Veeck L. R. Diaz E. Kaiser C. M Carvalho B.K. Veettil S. T. L. Costa R. C. Cruz D. Robérti S. B. A. Rolim Laboratório de Sensoriamento Geológico (LabSRGeo) Postgraduate in Remote Sensing UFRGS (Federal University of Rio Grande do Sul) Porto Alegre (RS) Brazil Image Processing Laboratory (IPL) UV (University of Valencia) Unidad de Cambio Global (UCG) Valencia Spain. Department of Physics UFSM (Federal University of Santa Maria) Santa Maria (RS) Brazil Laboratorio Interdisciplinar de Ciências Ambientais (LICA) UNIPAMPA (Federal University of Pampa) São Gabriel (RS) Brazil. Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City Vietnam

ISBN: (数字)9781728143507

Ecosystem evapotranspiration (ET) has been quantified around the world by different methodologies to understand the energy balance, especially to control the evolution of climate change. It is known that the vegetation of the pampa biome is natural grasslands, it has a large variety of species (flora and fauna), however is it different in the environmental aspects related to the energy balance when compared to the grassland cultivated? In this study the objective was to analyze the environmental differences of the Pampa Biome related to the energy balance in comparison with the pastures cultivated in Barrax, Spain. In the first one the minimum daily ET is 0.99 mm/day, while in the second is 1.57 mm/day. However, the highest differences between the sites occur during the summer period, in the maximum daily ET, the maximum is 16.25 mm/day in Pampa and in Barrax is 7. 31mm/day. The results of this study have indicated that the characteristics of the Pampa biome, both in terms of soil and climatic issues and land use, generate differences in the energy balance when compared to similar vegetation in other regions of the world.

关键词： Vegetation mapping Land surface remote sensing Meteorology Ecosystems Land surface temperature Heating systems

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A DEEP NETWORK APPROACH TO MULTITEMPORAL CLOUD DETECTION

A DEEP NETWORK APPROACH TO MULTITEMPORAL CLOUD DETECTION

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IEEE International Geoscience and remote sensing Symposium

作者： Devis Tuia Benjamin Kellenberger Adrian Perez-Suay Gustau Camps-Valls Laboratory of GeoInformation Science and Remote Sensing Wageningen University The Netherlands Image Processing Laboratory Universitat de València iSpain

We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

关键词： Clouds Feature extraction Training Predictive models Time series analysis Recurrent neural networks Computational modeling

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Linear unmixing for subsurface estimation on coastal shallow waters

Linear unmixing for subsurface estimation on coastal shallow...

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8th International Conference of Pattern Recognition Systems (ICPRS 2017)

作者： Emmanuel Carpena-Colón Luis O. Jiménez-Rodríguez Emmanuel Arzuaga-Cruz Laboratory for Applied Remote Sensing and Image Processing (LARSIP) Electrical and Computer Engineering Department University of Puerto Rico - Mayaguez Campus

ISBN: (纸本)9781785616525

This paper presents the development and enhancement of a subsurface (underwater) linear unmixing algorithm, called LIGU, specially conceived to determine individual contributions to the measured signal of given spectral reflectance of objects at the bottom of coastal shallow waters. This algorithm is part of a Hyperspectral Coastal image Analysis Toolbox (HyCIAT), which is a repository of tools to be used to retrieve information from object embedded in a diffusive and murky medium. This paper discusses mathematical formulations behind the subsurface unmixing algorithm LIGU and presents enhancements made to the algorithm. Finally, quantitative and qualitative results will be presented using a hyperspectral data set from a controlled and well known environment. These results provide noticeable quantitative improvement when compared with the same algorithms without the enhancements.

关键词： Linear Unmixing Hyperspectral Data Subsurface sensing Inverse Methods Estimation

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