Multi-spectral Image Panchromatic Sharpening, Outcome and Process Quality Assessment Protocol
This work provides a standardized framework for remote sensing researchers and practitioners to assess sharpening algorithms, though it is incremental as it builds on existing quality assessment methods.
The study addresses the lack of a standard evaluation procedure for multispectral image panchromatic sharpening by proposing a novel protocol for quality assessment of both product and process, including a new categorization of indicators and an innovative third-order isotropic multi-scale GLCM calculator.
Multispectral (MS) image panchromatic (PAN) sharpening algorithms proposed to the remote sensing community are ever increasing in number and variety. Their aim is to sharpen a coarse spatial resolution MS image with a fine spatial resolution PAN image acquired simultaneously by a spaceborne or airborne Earth observation (EO) optical imaging sensor pair. Unfortunately, to date, no standard evaluation procedure for MS image PAN sharpening outcome and process is community agreed upon, in contrast with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines proposed by the intergovernmental Group on Earth Observations (GEO). In general, process is easier to measure, outcome is more important. The original contribution of the present study is fourfold. First, existing procedures for quantitative quality assessment (Q2A) of the (sole) PAN sharpened MS product are critically reviewed. Their conceptual and implementation drawbacks are highlighted to be overcome for quality improvement. Second, a novel (to the best of these authors' knowledge, the first) protocol for Q2A of MS image PAN sharpening product and process is designed, implemented and validated by independent means. Third, within this protocol, an innovative categorization of spectral and spatial image quality indicators and metrics is presented. Fourth, according to this new taxonomy, an original third order isotropic multi scale gray level co occurrence matrix (TIMS GLCM) calculator and a TIMS GLCM texture feature extractor are proposed to replace popular second order GLCMs.