Please contact Tao Liu (firstname.lastname@example.org), Dalton Lunga (email@example.com) or Lexie Yang (firstname.lastname@example.org), if you're interested in presenting your paper in our session.
Deep learning, a subbranch of machine learning became famous in the computer vision community since Deep Convolutional Neural Networks (DCNN) revolutionized the supervised learning problem by introducing significant breakthroughs in the 2010 Large Scale Visual Recognition Challenge. Since then, deep learning techniques have been rapidly deployed in many other industrial and academic fields in recent years as it continues to advance technologies in areas like speech recognition, medical diagnosis, autonomous driving and even the gaming world, with or without combining the DCNN with techniques such as Long Short Term Memory(LSTM), Generative Adversarial Networks(GAN), reinforcement learning(RL), etc. Accompanying the fast development of deep learning techniques is the increased availability of computing equipment (e.g., GPU and hyper computers) and large volume of remote sensing data that has been continuously collected by existing and emerging sensors mounted on various remote sensing platforms. The success of deep learning has motivated researchers in the remote sensing community to investigate its usefulness for remote sensing image analysis. Therefore, it’s time to take a new look at the existing techniques for processing the remote sensing imagery by exploiting the recent developments of the algorithm and hardware in deep learning area.
This session aims to provide a platform for remote sensing researchers to share their new research discoveries and thoughts about deep learning applications in remote sensing fields. Example topics include but are not limited to:
(1) Deep learning applications for traditional remote sensing tasks such as landcover mapping, object detection, and change analysis.
(2) Novel deep learning architectures developed for remote sensing data analysis
(3) Novel framework designed to trigger the power of deep learning for remote sensing applications by satisfying the demands of massive training dataset for training deep learning networks.
(4) Exploration of various deep learning architectures such as Generative Adversarial Networks(GAN), Fully Convolutional Networks(FCN), Region-based Convolutional Neural Networks(RCNN), DCNN for remote sensing data analysis
(5) Combination of DCNN and Long Short Term Memory(LSTM) for temporal remote sensing data analysis
(6) Using deep learning techniques to urban infrastructure, such as building and road
|Presenter||Jian Liang*, School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China, Lin Li, School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China, Haihong Zhu, School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China, An Improved CNN for Extracting Buildings from High-Resolution Imagery||20||2:35 PM|
|Presenter||Congliang Zhou*, Texas Tech University, Guofeng Cao, Texas Tech University, An Unsupervised Deep Learning Approach for Remote Sensing Imagery Downscaling||20||2:55 PM|
|Presenter||Enbo Zhou*, Department of Geography, University of California, Santa Barbara, Vena W. Chu, Department of Geography, University of California, Santa Barbara, Samira Daneshgar, Department of Geography, University of California, Santa Barbara, Kang Yang, School of Geography and Ocean Science, Nanjing University, A Deep Learning Method for Extracting Supraglacial Streams on Greenland Ice Sheet||20||3:15 PM|
|Presenter||Gourav Jhanwar*, Purdue University, Morteza Karimzadeh, Purdue University, David Ebert, Purdue University, Calvin Yau, Purdue University, Kirubel Tadesse, Purdue University, Irrigation Management Using Deep Learning of Soil Moisture||20||3:35 PM|
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