Evaluation of different machine learning algorithms for LULC classification in heterogeneous landscape by using remote sensing and GIS techniques

Abstract

Abstract Remote sensing land cover classification plays a crucial role in detecting changes, urbanization planning, mapping and monitoring land cover on earth surface. It is very challenging to get accurate result in remote sensing data because different classifiers are very much area dependent. Different classifiers such as decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), boosted decision tree (BDT), random forest (RF), classification and regression tree (CART) and maximum likelihood classifiers (MLC), have different accuracies for the same classes. Several studies have utilized remote sensing and GIS tools to investigate changes in land use and land cover (LULC) using different classifiers. Seasonal rivers which should be classified as water bodies are mostly classified as urban area with the conventional land cover classification schemes because spectral reflectance of these river bodies is similar to urban area due to stones present in their river bed. It is very difficult to distinguish between these river beds (which are mostly found in various districts of Uttarakhand, India) and urban area in remote sensing images. In this paper, we present a new method to distinguish these river beds with the urban area and to separate other classes easily. First of all different spectral indices such as NDVI, NDBI, EVI and MNDWI are extracted from a high resolution Sentinel-2 MSI image then these indices are integrated with Sentinel-2 MSI image for classification of different land cover classes by using four different machine learning classifiers such as RF, SVM, DT and CART. The obtained results confirm the performance strength of the suggested method as there is much improvement in accuracy.