DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification

TL;DR

This paper introduces DDIPNet and DDIPNet+, innovative deep learning architectures that combine Deep Image Prior and Triplet Networks to improve remote sensing image classification, achieving state-of-the-art results on public datasets.

Contribution

The paper presents two novel architectures that integrate Deep Image Prior with Triplet Networks, advancing remote sensing image classification methods.

Findings

Achieved state-of-the-art accuracy on three public datasets.

Demonstrated the effectiveness of deep image priors in remote sensing.

Validated the proposed models' superiority over existing methods.

Abstract

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.