A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification

TL;DR

This paper introduces a multi-scale attention feature extraction block that enhances the classification stability and accuracy of very high-resolution aerial remote sensing images, especially for complex and small objects.

Contribution

It proposes a novel plug-and-play multi-scale attention feature extraction block (MSAFEB) that improves representation of VHR RS images with complex details.

Findings

Achieved high classification accuracy on AID and NWPU datasets.

Demonstrated stable performance with minimal standard deviation.

Outperformed existing methods in classification stability.

Abstract

Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the remote sensing 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 multi-scale attention feature extraction block (MSAFEB) based on multi-scale 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 of $0.002$) and competent overall classification performance (AID: 95.85\% and NWPU: 94.09\%).