Table 2 - uploaded by Siniša Drobnjak
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Source publication
In this study, land cover types in mountain Avala, Belgrade (Serbia), test area were analysed on the basis of the classification results acquired using the pixel based and object-oriented image analysis approaches. SPOT 5 with 4 spectral bands was used to carry out the image classification and ground truth data were collected from the available map...
Similar publications
The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data-abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 sc...
Citations
... Confusion (error) matrix is frequently used for standard pixel-based accuracy assessment. Confusion matrix is simple cross tabulation of the predicted class label against the reference data for a sample of cases at the specific locations, it provides an foundation on which both classification accuracy and characterize errors can be define [2,17]. ...
... With the usage of confusion matrix, we get a coefficient of kappa statistics which is a good indicator of the choice of classification method consistency taking their randomness into account. Kappa coefficient (κ) represents a coefficient which expresses a degree of compatibility between assigned classes by removing the misclassification [17]. ...
This article is connected with methods for automatic vegetation extraction using combination of multispectral satellite images and aerial photogrammetric images. Main goal of this paper is to suggest an optimal solution for easy mapping and updating vegetation areas on topographic maps. Vegetation areas are extracted on two ways: by visual interpretation and mapping using machine learning classification methods of satellite images and aerial photogrammetric images. Comparison of the results between these two approaches showed large geometric similarity. In this study, vegetation areas were extracted on the basis of the classification results acquired by machine learning algoritms approaches. Combination of SENTINEL-2A with 12 spectral bands satellite images and aerial photogrammetric images were used to carry out the image classification and ground sample data were collected from the available maps and personal knowledge. Machine learning classification algorithms were evaluated through ERDAS Imagine and R software. During the implementation, several different machine learning algorithms were tested for image segmentation and classification such as: Support Vector Machine-SVM, Random Forest-RF and Artifacial Neural Network-ANN. Results obtained from vegetation classification process showed that the Random Forest approach gave the most accurate results (including higher producer`s and user`s accuracy for most of the vegetation classes) for vegetation extraction using satellite and aerial photogrammetric images. Average classification accuracies were typically around 90-95%.
The primary objective of this conference article is to review the technological procedure of gathering vegetation cover from aerial photogrammetric and satellite images and obtaining information in digital form in order to model digital topographic maps and augment the database of spatial data on vegetation cover (the areas of the map that change the fastest and most dramatically).
