A support vector machine object based image analysis approach on urban green space extraction using Pleiades-1A imagery

Abstract

The use of remote sensing data for urban studies has increased along with the availability of Very High-Resolution (VHR) satellite data such as IKONOS, Quickbird, Worldview, and the Pleiades. This study aimed to evaluate the use of Pleiades-1A imagery and object based image analysis (OBIA) method to extract the information of urban green spaces in some areas of Jakarta, Indonesia. Multiresolution segmentation and spectral difference segmentation were then applied to the imagery respectively. Support Vector Machine (SVM) was performed for the classification phase, followed by an expert-knowledge refinement. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI) were derived from the imagery to help the classification process. The results showed two classes of landcover, that consists of “urban green” and “non-urban green”. The accuracy assessment was then performed using the visual interpretation followed by field measurements as reference data. By using the area-based similarity measurement framework, this study scored 86 % for overall accuracy. The similarity measurement showed values above 87 % for all 20 samples. This study found that the proposed methods gave a more into “similar” results to the reference data, than the “dissimilar”. The segmentation and classification rule set built in this study still need further study to see how effective the proposed method when applied to different cities with a different landuse/landcover characteristic.