Pairwise Comparison Network for Remote Sensing Scene Classification

TL;DR

This paper introduces a pairwise comparison network for remote sensing scene classification that improves accuracy by focusing on differences between image pairs, utilizing pairwise selection and representation methods.

Contribution

It proposes a novel pairwise comparison network with pairwise selection and representation, including self- and mutual-representations, to enhance remote sensing scene classification accuracy.

Findings

Effective on AID and NWPU-RESISC45 datasets

Outperforms existing methods in accuracy

Highlights subtle differences between images

Abstract

Remote sensing scene classification aims to assign a specific semantic label to a remote sensing image. Recently, convolutional neural networks have greatly improved the performance of remote sensing scene classification. However, some confused images may be easily recognized as the incorrect category, which generally degrade the performance. The differences between image pairs can be used to distinguish image categories. This paper proposed a pairwise comparison network, which contains two main steps: pairwise selection and pairwise representation. The proposed network first selects similar image pairs, and then represents the image pairs with pairwise representations. The self-representation is introduced to highlight the informative parts of each image itself, while the mutual-representation is proposed to capture the subtle differences between image pairs. Comprehensive experimental results on two challenging datasets (AID, NWPU-RESISC45) demonstrate the effectiveness of the proposed network. The codes are provided in https://github.com/spectralpublic/PCNet.git.