Pairwise Comparison Network for Remote Sensing Scene Classification
This work addresses classification accuracy issues in remote sensing for applications like environmental monitoring, but it is incremental as it builds on existing CNN methods with a novel pairwise approach.
The paper tackles the problem of confused images in remote sensing scene classification by proposing a pairwise comparison network that selects similar image pairs and represents them to capture subtle differences, achieving improved performance on datasets like AID and NWPU-RESISC45.
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.