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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

FR2.MM-9.1

Exploring a Deep Convolutional Neural Network and GEOBIA for Automatic Recognition of Brazilian palm swamps (Veredas) using Sentinel-2 optical data

Hugo Bendini, National Institute for Space Research (INPE), Brazil; Leila Fonseca, National Institute for Space Research, Brazil; Raian Maretto, Universiteit Twente (UT), Netherlands; Bruno Matosak, Evandro Taquary, Philipe Simões, National Institute for Space Research, Brazil; Ricardo Haidar, Federal University of Tocantins, Brazil; Dalton Valeriano, National Institute for Space Research, Brazil
Session:
Image Classification for Vegetation and Agriculture
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Fri, 16 Jul, 11:00-11:05 (UTC)
Fri, 16 Jul, 13:00-13:05 Central Europe Summer Time (UTC +2)
Fri, 16 Jul, 19:00-19:05 China Standard Time (UTC +8)
Fri, 16 Jul, 07:00-07:05 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
Giovanni Laneve, Sapienza Università di Roma and Jun Li, Sun Yat-Sen University
Session Manager:
Raj Kishore Parida
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Session FR2.MM-9
FR2.MM-9.1: Exploring a Deep Convolutional Neural Network and GEOBIA for Automatic Recognition of Brazilian palm swamps (Veredas) using Sentinel-2 optical data
Hugo Bendini, National Institute for Space Research (INPE), Brazil; Leila Fonseca, National Institute for Space Research, Brazil; Raian Maretto, Universiteit Twente (UT), Netherlands; Bruno Matosak, Evandro Taquary, Philipe Simões, National Institute for Space Research, Brazil; Ricardo Haidar, Federal University of Tocantins, Brazil; Dalton Valeriano, National Institute for Space Research, Brazil
FR2.MM-9.2: Surveying Green Spaces in European Human Settlements at 30 m Sub-Pixel Level
Fei Xu, Ben Somers, KU Leuven, Belgium
FR2.MM-9.3: WOODLAND SEGMENTATION OF GAOFEN-6 REMOTE SENSING IMAGES BASED ON DEEP LEARNING
Yuanyuan Gui, Wei Li, Mengmeng Zhang, Beijing Institute of Technology, China; Anzhi Yue, Chinese Academy of Sciences, China
FR2.MM-9.4: DEMONSTRATION OF WILDFIRE DETECTION USING IMAGE CLASSIFICATION ONBOARD CUBESAT
Muhammad Hasif bin Azami, Necmi Cihan Orger, Victor Hugo Schulz, Kitsune Members, Mengu Cho, Kyushu Institute of Technology, Japan
FR2.MM-9.5: NEW APPROACH OF SAMPLE GENERATION AND CLASSIFICATION FOR WILDFIRE FUEL MAPPING ON HYPERSPECTRAL (PRISMA) IMAGE
Riyaaz Uddien Shaik, Giovanni Laneve, Lorenzo Fusilli, Sapienza Università di Roma, Italy
FR2.MM-9.6: TREE SPECIES MAPPING IN TROPICAL FORESTS USING HYPERSPECTRAL REMOTE SENSING AND MACHINE LEARNING
Anushree Badola, University of Alaska Fairbanks, United States; Hitendra Padalia, Indian Institute of Remote Sensing, ISRO, Dehradun, India; Mariana Belgiu, University of Twente, Netherlands; Prabhakar Alok Verma, Indian Institute of Remote Sensing, ISRO, Dehradun, India
FR2.MM-9.7: A DESCRIPTOR TO SEPARATE URBAN TARGETS WITH LARGE AZIMUTH ORIENTATION ANGLES FROM VEGETATION TARGETS IN POLSAR DATA
Dingfeng Duan, University of Electronic Science and Technology of China, China; Yong Wang, Hong Li, East Carolina University, United States

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