Download Deliverable D2.2 Methodology specification for the

 Before going into a deeper discussion, it must be stated, that there is no such “raster mathematics” method in the classical remote sensing. There is no single specific satellite image classification concept or algorithm, which lays in the basis of raster mathematics. The paradox is that in image processing raster mathematics means nothing and everything at the same time. It does nothing specific for image classification, but one can do practically everything by using tools of raster mathematics. Looking back into the current report, we can notice that whatever image classification method is considered, there is one fundamental technology behind each of them – re‐calculation and re‐coding of pixel values into certain classes, which is nothing else, but the “grass‐roots” raster mathematics. While talking about the remote sensing applications, there is certainly no specific definition of raster mathematics method, as it includes any kind of manipulation of pixel values within a single raster layer, or between any number of overlapping raster layers. Raster mathematics is just a powerful framework for construction customized algorithms and development of highly sophisticated scripts for various remote sensing solutions. From the practical point of view, raster mathematics approach provides a very powerful and flexible toolbox for developers and advanced users to carry out almost any kind of spatial data “hacking” and develop completely automated and highly efficient image processing work‐flows. I.1.4.1. Raster mathematics methods We can distinguish several types of operations – all included into the raster mathematics engine of the software: −
Raster algebra covers a broad range of elementary mathematical operations, like adding, subtraction, multiplication, division, as well as all advanced mathematical and trigonometric functions. In fact, almost any mathematical formula can be implemented by those functions. Raster algebra functions can be applied only if more than one overlapping raster layer is used in the algorithm; −
Logical operators, like “if”, “and”, “or”, “not”, or their combinations allow the construction of sophisticated filters to be constructed within a pool of several overlapping raster layers. This filtering engine facilitates one of the most important principles of image classification, like co‐occurrence or exclusion of spatial objects; −
Moving window filters can be constructed with raster mathematics tools by defining custom raster window sizes and applying mathematical operations or statistical functions, as well as various re‐coding operations with customized weighting parameters, etc. This extremely powerful functionality can be applied on one raster layer, as well as several overlapping layers; −
Sequencing of raster mathematics operations allow construction of complex processing cycles (conditional loops), conditional branching and even machine learning by iterative analysis of intermediate results compared to the control points. All this provides a powerful framework for development of completely automated image processing chains – an ideal tool for industrial data processing solutions. There are several obligatory technical requirements while working with raster mathematics engines: −
Only raster datasets can be processed, which means that any ancillary or source datasets must be transformed into raster before entering the processing remote sensing software, D2.2 – Methodology specification for the harmonization of the available datasets 74