A Novel Multiscale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification

被引：0

作者：

Sitaula C. ^{[1
]}

Aryal J. ^{[1
]}

Bhattacharya A. ^{[2
]}

机构：

[1] The University of Melbourne, Earth Observation and Ai Research Group, Department of Infrastructure Engineering, Melbourne, 3010, VIC

[2] Indian Institute of Technology Bombay, Centre of Studies in Resources Engineering (CSRE), Mumbai

来源：

IEEE Geoscience and Remote Sensing Letters | 2023年 / 20卷

关键词：

Aerial image classification; deep learning (DL); feature extraction; remote sensing (RS);

D O I：

10.1109/LGRS.2023.3312643

中图分类号：

学科分类号：

摘要：

Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the RS community as it provides valuable spatial information for decision-making. Existing works on VHR aerial RS image classification produce an excellent classification performance; nevertheless, they have a limited capability to well-represent VHR RS images having complex and small objects, thereby leading to performance instability. As such, we propose a novel plug-and-play multiscale attention feature extraction block (MSAFEB) based on multiscale (MS) convolution at two levels with skip connection, producing discriminative/salient information at a deeper/finer level. The experimental study on two benchmark VHR aerial RS image datasets (AID and NWPU) demonstrates that our proposal achieves a stable/consistent performance (minimum standard deviation (SD) of 0.002) and competent overall classification performance (AID: 95.85% and NWPU: 94.09%). © 2004-2012 IEEE.

引用

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