This session is full.
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||Andong Ma*, Texas A&M University, Anthony M. Filippi, Texas A&M University, Zhengcong Yin, Texas A&M University, A novel recurrent neural networks based framework for hyperspectral image classification||20||12:40 PM|
|Presenter||Zhenlong LI*, University of South Carolina, Huan Ning, University of South Carolina, Using existing data to build customized deep learning training datasets for remote sensing image classification||20||1:00 PM|
|Presenter||St. Thomas LeDoux*, , Jeanette Weaver, ORNL, Dalton Lunga, ORNL, Jacob Arndt, ORNL, Sarah Tennille, ORNL, Data infrastructure for modeling regional metropolitan land use variation via deep learning and remote sensing||20||1:20 PM|
|Presenter||Lin LI*, School of Resource and Environment Sciences, Wuhan University, Jiang Liang, School of Resource and Environment Sciences, Wuhan University, Haihong Zhu, School of Resource and Environment Sciences, Wuhan University, Min Weng, School of Resource and Environment Sciences, Wuhan University, An Improved CNN for Extracting Buildings from High-resolution Imagery||20||1:40 PM|
|Presenter||Yun Li*, george mason university, Yongyao Jiang, george mason univerity, Improving Geospatial Data Search Ranking using Deep Learning and User Behaviour Data||20||2:00 PM|
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