Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification
Author:
Affiliation:
1. College of Information Science and Engineering, Ocean University of China, Qingdao, China
2. Data Science in Earth Observation (SiPEO, Former: Signal Processing in Earth Observation), Technical University of Munich (TUM), Munich, Germany
Funder
Alexander von Humboldt-Stiftung
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10295515.pdf?arnumber=10295515
Reference75 articles.
1. Computationally Lightweight Hyperspectral Image Classification Using a Multiscale Depthwise Convolutional Network With Channel Attention
2. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors;wang;arXiv 2207 02696,2022
3. MobileNets: Efficient convolutional neural networks for mobile vision applications;howard;arXiv 1704 04861,2017
4. ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
5. GhostNet: More Features From Cheap Operations
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