Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
International Journal of Remote Sensing
School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville 3209, Pietermaritzburg, South Africa; School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand - Johannesburg, Wits 2050, Johannesburg, South Africa
Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the 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: first, 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 second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers' cover maps with respective independent validation data sets. 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 similarly 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 of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount. © 2014 Taylor & Francis.
Decision trees; Image classification; Image resolution; Maps; Classification accuracy; Classification technique; Coastal landscapes; Confusion matrices; High spatial resolution; Machine-learning; Operational applications; Remotely sensed data; Support vector machines; accuracy assessment; coastal landform; image classification; image resolution; land cover; land use planning; mapping; performance assessment; satellite imagery; spatial distribution; vegetation cover
CSIR, Council of Scientific and Industrial Research; DST, Council of Scientific and Industrial Research; Council of Scientific and Industrial Research; NRF, Council of Scientific and Industrial Research