Robust Spatiotemporal Fusion of Satellite Images via Convex Optimization

Spatiotemporal fusion (ST fusion) is a feasible solution to resolve a tradeoff between the temporal and spatial resolutions of satellite images. Although many ST fusion methods have been proposed, most methods have not been developed that explicitly take noise in observed images into account, despite the inevitable influence of noise caused by the observation equipment and environment. In this paper, we propose an optimization-based ST fusion method that is robust to noise. First, we introduce observation models for noisy satellite images and make certain assumptions on the relationship between the observed images and the target high-resolution image. Next, based on these models and assumptions, we formulate the fusion problem as a constrained optimization problem and develop an efficient algorithm based on a primal-dual splitting method for solving the problem. The performance of the proposed method was verified using simulated and real data, and the results illustrate that our method outperforms state-of-the-art ST fusion methods for both noiseless and noisy satellite images.

1. INTRODUCTION

The analysis of multi-temporal image series is necessary and important in many remote sensing applications, such as vegetation/crop monitoring and estimation [1], evapotranspiration estimation [2], atmosphere monitoring [3], land-cover/land-use change detection [4], surface dynamic mapping [5], ecosystem monitoring [6], and soil water content analysis [7].