This repo contains the codes for the IGARSS2020 paper: Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification. we propose a novel CNN architecture for the feature embedding of high-dimensional RS images. The proposed architecture aims at: 1) decoupling the spectral and spatial feature extraction for sufficiently describing the complex information content of images; and 2) taking advantage of multi-scale representations of different land-use and land-cover classes present in the images. To this end, the proposed architecture is mainly composed of: 1) a convolutional layer for band-wise extraction of multi-scale spatial features; 2) a convolutional layer for pixel-wise extraction of spectral features; and 3) standard 2D convolution and residual blocks for further feature learning. Experiments on BigEarthNet validate the effectiveness of the proposed method, when compared to the state-of-the-art CNN architectures.
An illustration of the architecture of the proposed BWMS. BWMS consists of three modules: 1) spatial feature extraction based on band-wise multi-scale convolution; 2) spectral feature extraction based on pixel-wise convolution; and 3) standard 2D convolutional and residual blocks for higher-level feature learning.
Learning curves of all the CNN architectures.
KB_MDP_RS18_FF_LReLU is the CNN architecture used in the paper.
KB_MDP_RS50_FF_LReLU is the ResNet50 version of the proposed architecture.
@article{kang2020igarss,
title={{Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification}},
author={Kang, Jian and Demir, Begüm},
booktitle={IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium},
pages={},
year={2020},
organization={IEEE}
}