MOP1.PB.6
A LIGHT CNN BASED ON RESIDUAL LEARNING AND BACKGROUND ESTIMATION FOR HYPERSPECTRAL ANOMALY DETECTION
Jiajia Zhang, Pei Xiang, Jin Shi, Xiang Teng, Xidian University, China; Dong Zhao, Wuxi University, China; Huixin Zhou, Huan Li, Jiangluqi Song, Xidian University, China
Session:
MOP1.PB: Advanced Signal Processing Methods for Geoscience and Remote Sensing Applications I Poster
Track:
Community-Contributed Sessions
Location:
Poster Area
Presentation Time:
Monday, 4 August, 14:15 - 15:45
Session Co-Chairs:
Donny Danudirdjo, Bandung Institute of Technology and Akira Hirose, The University of Tokyo
Presentation
Discussion
Resources
No resources available.
Session MOP1.PB
MOP1.PB.1: PATTERN-COUPLED BAYESIAN SUPERRESOLUTION FORWARD-LOOKING IMAGING FOR MANEUVERING TRAJECTORY UNDER OPTIMIZED CONVOLUTION MODEL
Yiheng Guo, Yujie Liang, Tan Wang, Zuokun Tao, Yi Liang, Xidian University, China
MOP1.PB.2: Fine-Tuning-Based Zero-Shot Pansharpening with Residual Double Attention Network
Rika Sustika, National Research and Innovation Agency, Indonesia; Andriyan Bayu Suksmono, Donny Danudirdjo, Ketut Wikantika, Bandung Institute of Technology, Indonesia
MOP1.PB.3: Joint MTRC and Non-Uniform Rotational Motion Compensation Method for Sparse ISAR Imaging of Maneuvering Targets
Yue Wang, Xueru Bai, Xidian University, China
MOP1.PB.4: Intra-pulse Forwarding Jamming Detection and Recognition in Clutter Environment
Yukai Kong, Xianxiang Yu, Jiachen Li, Guolong Cui, University of Electronic Science and Technology of China, China
MOP1.PB.5: ESTIMATING SOIL MOISTURE AND BIOMASS IN MAIZE FIELDS FROM L-BAND RADAR DATA BY INVERSION OF A FULL-WAVE MODEL
A. Kaleo Roberts, Kamal Sarabandi, The University of Michigan, United States
MOP1.PB.6: A LIGHT CNN BASED ON RESIDUAL LEARNING AND BACKGROUND ESTIMATION FOR HYPERSPECTRAL ANOMALY DETECTION
Jiajia Zhang, Pei Xiang, Jin Shi, Xiang Teng, Xidian University, China; Dong Zhao, Wuxi University, China; Huixin Zhou, Huan Li, Jiangluqi Song, Xidian University, China
Resources
No resources available.