Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage of the world’s total cultivated surface. To characterize and analyze this type of landscape, multispectral (MS) images with high and very high spatial resolutions are required. Multi-source image fusion algorithms are normally used to improve the spatial resolution of images with a medium spatial resolution. In particular, pansharpening (PS) methods allow one to produce high-resolution MS images through a coherent integration of spatial details from a panchromatic (PAN) image with spectral information from an MS. The spectral and spatial quality of source images must be preserved to be useful in RS tasks. Different PS strategies provide different trade-offs between the spectral and the spatial quality of the fused images. Considering that agricultural landscape images contain many levels of significant structures and edges, the PS algorithms based on filtering processes must be scale-aware and able to remove different levels of detail in any input images. In this work, a new PS methodology based on a rolling guidance filter (RGF) is proposed. The main contribution of this new methodology is to produce artifact-free pansharpened images, improving the MS edges with a scale-aware approach. Three images have been used, and more than 150 experiments were carried out. An objective comparison with widely-used methodologies shows the capability of the proposed method as a powerful tool to obtain pansharpened images preserving the spatial and spectral information