Remotely Sensed Image Fast Classification and Smart Thematic Map Production

Abstract

Abstract. Apps available for Smartphone, as well as software for GNSS/GIS devices, permit to easily mapping the localization and shape of an area by acquiring the vertices coordinates of its contour. This option is useful for remote sensing classification, supporting the detection of representative sample sites of a known cover type to use for algorithm training or to test classification results. This article aims to analyse the possibility to produce smart maps from remotely sensed image classification in rapid way: the attention is focalized on different methods that are compared to identify fast and accurate procedure for producing up-to-date and reliable maps. Landsat 8 OLI multispectral images of northern Sicily (Italy) are submitted to various classification algorithms to distinguish water, bare soil and vegetation. The resulting map is useful for many purposes: appropriately inserted in a larger database aimed at representing the situation in a space-time evolutionary scenario, it is suitable whenever you want to capture the variation induced in a scene, e.g. burnt areas identification, vegetated areas definition for tourist-recreational purposes, etc. Particularly, pixel-based classification approaches are preferred, and experiments are carried out using unsupervised (k-means), vegetation index (NDVI, Normalized Difference Vegetation Index), supervised (minimum distance, maximum likelihood) methods. Using test sites, confusion matrix is built for each method, and quality indices are calculated to compare the results. Experiments demonstrate that NDVI submitted to k-means algorithm allows the best performance for distinguishing not only vegetation areas but also water bodies and bare soils. The resulting thematic map is converted for web publishing.

Estimating the Total Volume of Running Water Bodies Using Geographic Information System (GIS): A Case Study of Peshawar Basin (Pakistan)

“…In a study by Abdulwahd et al [33], analysis of the spatial data and DEM data was conducted using GIS to estimate the hydrological properties for the watershed valley with a 158.5 km 2 surface area. Alcaras et al [34] analyzed the possibility of rapidly producing smart maps from remotely sensed image classification. Amano and Iwasaki [35] used GIS data and SPOT 6/7 satellite images to classify the Kumamoto area into nine categories.…”

Modeling and Accuracy Assessment of Determining the Coastline Course Using Geodetic, Photogrammetric and Satellite Measurement Methods: Case Study in Gdynia Beach in Poland

“…The CM is structured as a table of values, in which the columns represent the predicted values, and the rows represent the real values, while the diagonal represents the values of the correctly classified pixels. Starting from CM, it is possible to calculate three accuracy values that permit this table to be summarized, called User Accuracy (UA), Producer Accuracy (PA), and Overall Accuracy (OA) [44].…”

The Effectiveness of Pan-Sharpening Algorithms on Different Land Cover Types in GeoEye-1 Satellite Images

“…The classification is therefore carried out by applying the maximum likelihood classification (MLC), a supervised classification technique [45] employing training sites to estimate statistical characteristics of the classes, which are used to evaluate probabilities that a pixel is assigned to a determinate class [46]. MLC is applied directly on NDVI.…”