ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume II-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-7, 31–37, 2014
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-7, 31–37, 2014

  19 Sep 2014

19 Sep 2014

Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images

T. Kavzoglu and M. Yildiz T. Kavzoglu and M. Yildiz
  • Gebze Institute and Technology, Department of Geodetic and Photogrammetric Engineering, 41400, Gebze-Kocaeli, Turkey

Keywords: Object Based Image Analysis, Segmentation, Nearest Neighbour Classification, VHR Images, Classification Accuracy

Abstract. Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.