FR4.M4.3
OPTIMIZING DEEP LEARNING FOR SATELLITE HYPERSPECTRAL DATA: AN XAI-DRIVEN APPROACH TO HYPERPARAMETER SELECTION
Michele Linardi, Sékou Dabo, CY Cergy Paris Université, France; Claudia Paris, University of Twente, ITC Faculty Geo-Information Science and Earth Observation, Netherlands
Session:
FR4.M4: Hyperspectral Image Analysis II Oral
Track:
Data Analysis
Location:
Room M4
Presentation Time:
Friday, 8 August, 16:15 - 16:30
Presentation
Discussion
Resources
No resources available.
Session FR4.M4
FR4.M4.1: HSCT: HIERARCHICAL SELF-CALIBRATION TRANSFORMER FOR HYPERSPECTRAL IMAGE SUPER-RESOLUTION
Jinliang Hou, Yifan Zhang, Yuanjie Zhi, Shaohui Mei, Northwestern Polytechnical University, China
FR4.M4.2: DEEP LEARNING METHOD FOR HYPERSPECTRAL IMAGE RECONSTRUCTION VIA ROTATED DIFFRACTION PATTERNS
James Brady, Akram Hourani, Sumeet Walia, Royal Melbourne Institute of Technology, Australia; Gabriele Meoni, European Space Agency, Italy; Taimur Ahmed, Irfan Abidi, Royal Melbourne Institute of Technology, Australia
FR4.M4.3: OPTIMIZING DEEP LEARNING FOR SATELLITE HYPERSPECTRAL DATA: AN XAI-DRIVEN APPROACH TO HYPERPARAMETER SELECTION
Michele Linardi, Sékou Dabo, CY Cergy Paris Université, France; Claudia Paris, University of Twente, ITC Faculty Geo-Information Science and Earth Observation, Netherlands
FR4.M4.4: MULTI-VIEW DEEP SUBSPACE CLUSTERING FOR HYPERSPECTRAL BAND SELECTION
Dongkai Yan, Chuanyu Cui, Xudong Sun, Xiaodi Shang, Qingdao University, China
FR4.M4.5: DEEP ADAPTIVE UNROLLING NETWORK FOR CODED APERTURE SNAPSHOT SPECTRAL IMAGING
Tangwei Lu, Xinyu Wang, Zengliang Zhu, Chen Sun, Yanfei Zhong, Wuhan University, China
Resources
No resources available.