Deep Gradient Projection Networks for Pan-sharpening (2103.04584v1)

Abstract: Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively. The codes are available at https://github.com/xsxjtu/GPPNN.

• The paper presents GPPNN, a dual-block deep learning framework that integrates PAN and LRMS image models for enhanced pan-sharpening.
• It employs gradient projection with deep prior regularization to achieve superior spatial and spectral fidelity.
• Extensive experiments across Landsat8, GaoFen2, and QuickBird datasets show GPPNN outperforms 13 traditional and advanced methods in key metrics.

Deep Gradient Projection Networks for Pan-sharpening: A Technical Overview

The paper presents a specialized paper on pan-sharpening, a technique pivotal for enhancing the resolution of multispectral images in remote sensing applications. While traditional methods have relied on component substitution and multiresolution analysis, there has been a notable pivot towards adopting deep learning frameworks, which are now the forefront of pan-sharpening research.

The authors introduce a model-based deep learning approach, the Gradient Projection Pan-sharpening Neural Network (GPPNN), which encapsulates both the characteristics of panchromatic (PAN) and low-resolution multispectral (LRMS) images. The GPPNN distinguishes itself from existing paradigms by employing a dual-block structure to integrate the various generative models for PAN and LRMS images separately, before converging them into a unified high-resolution output.

Methodology

The GPPNN fundamentally relies on gradient projection methods applied to the generative models of PAN and LRMS images. Specifically, these models are structured as two distinct optimization problems:

1. PAN Image Model: This sub-engine works on the hypothesis that PAN images are linear combinations of high-resolution multispectral images, regulated via spectral response functions.
2. LRMS Image Model: This leverages the framework where LRMS images result from blurring and downsampling operations on their high-resolution counterparts.

Each optimization problem is approached using deep prior regularizers, moving away from hand-tuned regularization tactics that have characterized classic methods such as total variation minimization. By employing deep priors within the gradient projection algorithm, the network not only achieves improved spatial and spectral fidelity but does so with notable interpretability of each computational step.

Implementation and Performance

The GPPNN is constructed by alternating these PAN and LRMS blocks through iterative processes akin to algorithm unrolling techniques, a concept that has captivated recent image processing tasks due to their transparency and theoretical validity. The efficacy of the GPPNN is demonstrated through extensive experimentation on datasets from Landsat8, GaoFen2, and QuickBird satellites. These experiments reveal superior visual and quantitative performance when benchmarked against 13 traditional and state-of-the-art methods.

• Quantitative Metrics: GPPNN excelled in fidelity measures such as PSNR, SSIM, and SAM across all datasets, reflecting its prowess in maintaining high spatial resolution and spectral coherence.
• Visual Inspection: The visual outputs from GPPNN exhibit fine textures and minimized artifacts, showcasing its effectiveness in real-world scenarios compared to its counterparts.

Conclusion and Future Directions

The work delineates the profound utility of model-driven architectures in deep learning for image fusion tasks like pan-sharpening. The implications are considerable for remote sensing, offering potential enhancements in satellite image processing and analysis. Future work may focus on extending the generalization capability of GPPNN across disparate satellite systems, as well as exploring hybrid architectures to meld traditional image preprocessing techniques with deep neural networks, potentially enhancing information retrieval and application accuracy.

In summary, the GPPNN embodies a strategic leap in pan-sharpening methodology, integrating robust model-based frameworks with the adaptive learning capabilities of modern neural networks to deliver superior results in multispectral image resolution enhancement.