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

• URL: http://arxiv.org/abs/2308.14076v1
• Date: Sun, 27 Aug 2023 11:49:46 GMT
• Title: A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification
• Authors: Chiranjibi Sitaula, Jagannath Aryal and Avik Bhattacharya
• Abstract summary: 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. 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%)
• Score: 9.388978548253755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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\%).

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