Abstract
Land cover classification has become an interesting research area in the field of remote sensing. Machine learning techniques have shown great success for various application in the domain of land cover classification. This paper focuses on the classification of land covers obtained from high resolution images using two well-known classification methods by integrating with object-based segmentation technique. First, graph-based minimal spanning tree segmentation was applied to segment the original image pixels into objects. The segmented objects were then used to obtained spectral, spatial and texture features which were then combined to form a single high dimensional feature vector. These features were then used to train and test the artificial neural network (ANN) and support vector machine (SVM). The proposed method was evaluated on a dataset consisting of high resolution multi-spectral images with four classes (tea area, other trees, roads and builds, bare land). The experiments showed that ANN was more accuracy as it scored average accuracy of 82.60% while SVM produced 73.66%. Moreover, when postprocessing using majority analysis was applied, the average accuracy improved to 86.18%.
Highlights
NDVI is a measurement that uses the plant's viability by exploiting its greenness information. This measurement is made by the difference between near infrared (NIR) reflected by vegetation and red light absorbed by vegetation
Support vector machines (SVM), artificial neural networks (ANN), decision trees, maximum likelihood, random forests are among the main algorithms used for classification
We investigated two well-known supervised approaches for land cover classification: SVM and ANN
Summary
Digital image processing and machine learning approaches are combined to derive useful information from images. Various methods are used to extract land cover from images obtained by remote sensing systems. Land cover maps obtained after the classification process can be used in areas such as geomorphology, Geographic Information Systems (GIS). Thanks to these maps, scientists can track [108]. We investigated two well-known supervised approaches for land cover classification: SVM and ANN. Both spectral and spatial features were derived from the high resolution images and fed into the classifier to obtain four different land cover classes: tea area, other trees, roads and builds, bare land
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