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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

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

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A Subpixel Target Detection Approach to Hyperspectral image Classification

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IEEE Transactions on Geoscience and remote sensing 2017年第9期55卷 5093-5114页

作者： Xue, Bai Yu, Chunyan Wang, Yulei Song, Meiping Li, Sen Wang, Lin Chen, Hsian-Min Chang, Chein-I Center for Hyperspectral Imaging in Remote Sensing Information and Technology College Dalian Maritime University Dalian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Key Laboratory of Spectral Imaging Technology Chinese Academy of Sciences Xi'an China State Key Laboratory of Integrated Services Networks Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science and Information Management Providence University Taichung02912 Taiwan

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral-spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu's method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. © 1980-2012 IEEE.

关键词： Support vector machines

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Detection of Fusarium wilt on Phalaenopsis stem base region using band selection techniques 38

Detection of Fusarium wilt on Phalaenopsis stem base region ...

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38th Annual IEEE International Geoscience and remote sensing Symposium, IGARSS 2018

作者： Lee, Meng-Chueh Ma, Kenneth-Yeonkong Ouyang, Yen-Chieh Ou-Yang, Mang Guo, Horng-Yuh Liu, Tsang-Sen Chen, Hsian-Min Wu, Chao-Cheng Chang, Chgein-I Department of Communications Engineering National Chung Hsing University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan National Chiao-Tung University Department of Electrical and Computer Engineering Hsinchu City Taiwan Taiwan Agriculture Research Institute Nan-Tou Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (纸本)9781538671504

Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt. © 2018 IEEE

关键词： Diseases Hyperspectral imaging Support vector machines Agriculture Databases Entropy

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Common Limitations of image processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs 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 Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg 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 Division of Medical Image Computing Heidelberg 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 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 Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom 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 Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： remote sensing

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Building Change Detection Improvement Using Topographic Correction Models

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Advances in remote sensing 2017年第1期6卷 1-22页

作者： Shabnam Jabari Yun Zhang CRC Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton Canada Institution of Remote Sensing and Geographic Information System Peking University Beijing China

In the change detection application of remote sensing, commonly the variation in the brightness values of the pixels/objects in bi-temporal image is used as an indicator for detecting changes. However, there exist effects, other than a change in the objects that can cause variations in the brightness values. One of the effects is the illumination difference on steep surfaces mainly steeproofs of houses in very high resolution images, specifically in off-nadir images. This can introduce the problem of false change detection results. This problem becomes more serious in images with different view-angles. In this study, we propose a methodology to improve the building change detection accuracy using imagery taken under different illumination conditions and different view-angles. This is done by using the Patch-Wise Co-Registration (PWCR) method to overcome the misregistration problem caused by view-angle difference and applying Topographic Correction (TC) methods on pixel intensities to attenuate the effect of illumination angle variation on the building roofs. To select a proper TC method, four of the most widely used correction methods, namely C-correction, Minnaert, Enhanced Minnaert (for slope), and Cosine Correction are evaluated in this study. The results proved that the proposed methodology is capable to improve the change detection accuracy. Specifically, the correction using the C-correction and Enhanced Minnaert improved the change detection accuracy by around 35% in an area with a large number of steep-roof houses imaged under various solar angles.

关键词： Topographic Correction Off-Nadir imagery Building Change Detection Patch-Wise Co-Registration (PWCR)

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Convex cone volume analysis for finding endmembers in hyperspectral imagery

Convex cone volume analysis for finding endmembers in hypers...

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作者： Chang, Chein-I Xiong, Wei Chen, Shih-Yu Information and Technology College Dalian Maritime University Dalian China School of Physics and Optoelectronic Engineering Xidian University Xian China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a convex cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Spectroscopy

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A SUN-INDUCED VEGETATION FLUORESCENCE RETRIEVAL METHOD FROM TOP OF ATMOSPHERE RADIANCE FOR THE FLEX/SENTINEL-3 TANDEM MISSION

A SUN-INDUCED VEGETATION FLUORESCENCE RETRIEVAL METHOD FROM ...

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IEEE International Geoscience and remote sensing Symposium

作者： N. Sabater L. Alonso S. Cogliati J. Vicent C. Tenjo J. Verrelst J. Moreno Image Processing Laboratoy (IPL) University of Valencia Image Processing Laboratory (IPL) University of Valencia Remote Sensing of Environmental Dynamics Laboratory University of Milano-Bicocca (Italy)

ISBN: (纸本)9781479979301

A new fluorescence retrieval method is proposed to support ESA's 8th Earth Explorer FLuorescence EXplorer/Sentinel-3 (FLEX-S3) candidate tandem mission. FLEX is the first mission specially dedicated to measure the Sun-Induced vegetation chlorophyll fluorescence (SIF) strongly related with the vegetation photosynthetic activity. Most hyperspectral fluorescence retrieval algorithms available in the literature are very sensitive to true reflectance modelization and/or they assume the atmospheric status as known. The proposed algorithm delivers the retrieval of full fluorescence spectrum at canopy level by using only Top Of Atmosphere (TOA) radiances from S3 and FLEX as input. Once the spatial co-registration and cross-calibration of S3 and FLEX images have been performed, the proposed method starts with (1) the atmospheric correction of TOA radiances, characterizing the state of the atmosphere, (2) performing a first estimation of fluorescence values in main oxygen absorption bands without any approximation of true reflectance spectrum, and using this fluorescence estimation to initialize a Spectral Fitting Method (SFM) to finally retrieving a full fluorescence spectrum. This proposed fluorescence retrieval method is currently being implemented at the Level-2 Retrieval Module (L2RM) of the FLEX/End-To-End Simulator (E2ES).

关键词： FLEX Fluorescence retrieval Atmospheric correction Sentinel-3 Synergy products

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High-order CRF based on product-of-experts for unsupervised SAR image multiclass segmentation

High-order CRF based on product-of-experts for unsupervised ...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Peng Zhang Ming Li Lin An Lu Jia Yan Wu Xidian University National Laboratory of Radar Signal Processing Xi‘ an China School of Electronic Engineering Xidian University Remote Sensing Image Processing and Fusion Group Xi'an China

ISBN: (纸本)9781467372985

Conditional random fields (CRF) model is suitable for image segmentation because this model directly defines the posterior distribution as a Gibbs field and allows one to capture the dependencies of the observed data. However, this model has a limited ability to capture the high-order label dependencies with only the pairwise potential being constructed generally. Moreover, for synthetic aperture radar (SAR) image segmentation, SAR scattering statistics that are essential to SAR image processing are not considered in CRF model. Then in this paper, we propose a high-order CRF model based on product-of-experts (POE) for unsupervised SAR image multiclass segmentation, named as HCRF-POE model. HCRF-POE model decomposes the high-order label dependencies into the low-order ones and constructs the high-order potential based on POE, thus effectively capturing high-order label dependencies. In addition, to capture SAR data information including the textural features and the scattering statistics, in a more completed manner, HCRF-POE model integrates SAR data information under unsupervised Bayesian framework. The effectiveness of HCRF-POE model is demonstrated by the application to the unsupervised segmentation of the simulated image and the real SAR images.

关键词： Decision support systems Conferences Apertures Yttrium

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Recursive Automatic Target Generation Process for Unsupervised Hyperspectral Target Detection

Recursive Automatic Target Generation Process for Unsupervis...

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IEEE International Geoscience and remote sensing Symposium

作者： Cheng Gao Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland

ISBN: (纸本)9781479953141

Automatic target generation process (ATGP) has been found very useful and effective for unsupervised target detection. It performs a sequence of orthogonal subspace projection to extract potential targets of interest. One major issue arises in ATGP is how to terminate the algorithm in the sense that how many targets are required for ATGP to generate before it is terminated. This paper presents a recursive version of ATGP, referred to as recursive ATGP (RATGP) which has two advantages. One is no need of inverting any matrix as ATGP does for finding each target. Most importantly, a stopping rule can be derived for ATGP via RATGP is also developed using the Neyman-Pearosn detection theory to determine how many targets needed to be generated by RATGP before it is terminated.

关键词： Automatic target generation process (ATGP) Recursive ATGP (RATGP)

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