The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.ĭevelopment and application of GIS-based PRISM integration through a plugin approach The new set of operations included in the PI2 GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. When analysing SCP, it was realized that a set of operations, that are very useful in teaching classes of remote sensing and image processing tasks, were lacking, such as the visualization of histograms, the application of filters, different image corrections, unsupervised classification and several environmental indices computation. SCP allows the supervised classification of remote sensing images, the calculation of vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) and other image processing operations. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. The application was integrated in a GIS software ( QGIS), automating several image processing steps. The aim of this work was to develop an open source application to automatically process and classify remote sensing images from a set of satellite input data. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System ( GIS) software. To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. PI2 GIS: processing image to geographical information systems, a learning tool for QGIS
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