A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification

TL;DR

This paper introduces a siamese CNN that combines classification and metric learning to improve remote sensing image scene classification by mapping similar scenes close together and different scenes farther apart in feature space.

Contribution

The work presents a novel siamese network architecture that jointly optimizes classification and metric learning losses for enhanced scene classification accuracy.

Findings

Achieves high classification accuracy on three remote sensing datasets.

Effectively separates intra-class and inter-class image pairs in feature space.

Demonstrates superior performance compared to baseline methods.

Abstract

In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to predict the classes of the images. For the metric learning loss, our siamese network learns to map the intra-class and inter-class input pairs to a feature space where intra-class inputs are close and inter-class inputs are separated by a margin. Concretely, for remote sensing image scene classification, we would like to map images from the same scene to feature vectors that are close, and map images from different scenes to feature vectors that are widely separated. Experiments are conducted on three different remote sensing image datasets to evaluate the effectiveness of our proposed approach. The results demonstrate that the proposed method achieves an excellent classification performance.