Declouding of Satellite Images for Crop Growth Monitoring Via Unrolling of Gradient Graph Laplacian Regularizer

Spectral images periodically captured by satellites are often obscured by clouds. For crop monitoring, cloud removal—called “declouding”—in satellite images is important, so that crop growth at a field level can be estimated from restored images. In this paper, we adopt a graph signal processing (GSP) approach to satellite image declouding to capture neighboring pixel correlations that persist over time. We first assume an atmospherical scattering model (ASM) for image formation, where observation $\mathbf{y}$ is a product of piecewise constant (PWC) target image x and piecewise planar (PWP) transmission map t. To decompose y back into $\mathbf{x}$ and $\mathbf{t}$, we formulate respective quadratic programming (QP) problems, without / with box constraints, using — graph Laplacian regularizer (GLR) / gradient graph Laplacian regularizer (GGLR) as prior, to compute $\mathbf{x} / \mathbf{t}$ alternately. For efficient optimization, we compute $\mathbf{x}$ in an unconstrained QP via conjugate gradient (CG) without matrix inverse, while we compute t in a constrained QP via proximal gradient descent (PGD). We unroll iterations of our alternating algorithm into neural layers for end-to-end data-driven parameter optimization, resulting in an interpretable, algorithm-specific feed-forward network. Experimental results show that our unrolled network outperforms model-based and pure deeplearning schemes in declouded image quality, objectively and subjectively.