TH2.M4.4
FLEXIBLE BACKGROUND MODELING FOR HYPERSPECTRAL ANOMALY DETECTION USING CONVOLUTIONAL SPARSE CODING
Koyo Sato, Kazuki Naganuma, Shunsuke Ono, Institute of Science Tokyo, Japan
Session:
TH2.M4: Hyperspectral Anomaly Detection Oral
Track:
Data Analysis
Location:
Mezzanine: Room M4
Presentation Time:
Thursday, 7 August, 11:15 - 11:30
Session Chair:
Ying Qu, Beijing Normal University
Session TH2.M4
TH2.M4.1: LOW-RANK AND SPARSITY-INDUCED AUTOENCODERS WITH SEPARABILITY ENHANCEMENT FOR HYPERSPECTRAL ANOMALY DETECTION
Yixin Yang, Xuan Liu, Xi'an University of Posts and Telecommunications, China
TH2.M4.2: PIXEL-ASSOCIATED AUTOENCODER FOR HYPERSPECTRAL ANOMALY DETECTION
Pei Xiang, Xidian University, China; Shahzad Ali, Kyungpook National University, Korea (South); Jiajia Zhang, Xidian University, China; Soon Ki Jung, Kyungpook National University, Korea (South); Huixin Zhou, Xidian University, China
TH2.M4.3: Accelerating Generative Adversarial Network for Hyperspectral Anomaly Detection
Kun Yu, Zebin Wu, Nanjing University of Science and Technology, China
TH2.M4.4: FLEXIBLE BACKGROUND MODELING FOR HYPERSPECTRAL ANOMALY DETECTION USING CONVOLUTIONAL SPARSE CODING
Koyo Sato, Kazuki Naganuma, Shunsuke Ono, Institute of Science Tokyo, Japan
TH2.M4.5: Frequency Domain Mask Guided Diffusion Model for Hyperspectral Anomaly Detection
Guorong Zhang, Tao Sun, Jie Yin, Wuhan University, China; Shaoquan Zhang, Nanchang Institute of Technology, China; Yuhao Wu, Wuhan University, China
Resources