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PI2GIS: processing image to geographical information systems, a learning tool for QGIS
Author(s): R. Correia; A. Teodoro; L. Duarte
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Paper Abstract

To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System (GIS) software. 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. The application was integrated in a GIS software (QGIS), automating several image processing steps. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. 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. 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. The new set of operations included in the PI2GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.

Paper Details

Date Published: 5 October 2017
PDF: 11 pages
Proc. SPIE 10428, Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 104281H (5 October 2017); doi: 10.1117/12.2277952
Show Author Affiliations
R. Correia, Univ. do Porto (Portugal)
A. Teodoro, Univ. do Porto (Portugal)
L. Duarte, Univ. do Porto (Portugal)


Published in SPIE Proceedings Vol. 10428:
Earth Resources and Environmental Remote Sensing/GIS Applications VIII
Ulrich Michel; Karsten Schulz, Editor(s)

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