Land use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

Abstract

Mapping pattern and spatial distribution of land use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives, firstly, to test the new generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape and secondly, to determine the importance of different RapidEye bands on the classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation dataset. An overall classification accuracy of 93.07% with a kappa value of 0.92 and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similar as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit for high classification accuracy using high spatial resolution imagery on complex landscapes remains paramount.