Large Kernel Sparse ConvNet Weighted by Multi-Frequency Attention for Remote Sensing Scene Understanding
Author:
Affiliation:
1. School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China
2. LJK, CNRS, INRIA, Grenoble INP, University of Grenoble Alpes, Grenoble, France
Funder
National Key Research and Development Program 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/ielx7/36/10006360/10319785.pdf?arnumber=10319785
Reference64 articles.
1. AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
2. CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism
3. Remote Sensing Image Scene Classification Using CNN-CapsNet
4. SEMSDNet: A Multiscale Dense Network With Attention for Remote Sensing Scene Classification
5. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
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