Last year we launched the HICO Image Processing System (HICO IPS) – a prototype web application for on-demand remote sensing data analysis in the cloud.
To demonstrate the capabilities of this system, we implemented a collection of coastal remote sensing algorithms to produce information on water quality, water depth and benthic features using example imagery from the HICO instrument on the International Space Station.
As the HICO IPS approaches its one year anniversary, and continues its excellent performance, we’d like to take a moment to highlight each of the algorithms currently implemented in the system.
Here we begin with an overview of the land/water mask utilized in the HICO IPS.
Objective – Implement an automated algorithm for classifying land versus water, thereby masking land pixels from further analysis and allowing subsequent processing steps to focus on just water pixels.
Algorithm – Generates a binary mask differentiating land from water using the Normalized Difference Water Index (NDWI; McFeeters 1996). This algorithm can be implemented on its own, or as a pre-processing step in other algorithm workflows.
Inputs – User specified HICO scene, with optional region-of-interest; and user adjustable NDWI threshold, where -1.0 ≤ NDWI ≤ 1.0, land ≤ threshold < water, and default threshold = 0.0.
Output – Binary land/water mask (0 = land; 1 = water), where land is displayed in the online map using a black mask and water remains unchanged.
Reference – McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, vol. 17(7), 1425-1432.
Try it out today for yourself: http://hyspeedgeo.com/HICO/