Village forms Classification by Object-Based Image Analysis

Abstract

This paper presents and discusses an application of object-based image analysis for rural land use/land cover classification based on village forms and shapes in Northeastern, Thailand. Increasing availability of VHRS data and object-based classification techniques can be extremely effective to delineate the complexity of land cover in the study area. Quick Bird pan-sharpened imagery with spatial resolution of 0.6 meters is being used in this study. First, a multi resolution segmentation algorithm was used for creating image objects from heterogeneous pixel values. Transportation network and topographic information were also incorporated as ancillary data into the segmentation procedure. It allows a creation of different levels of segments supporting a hierarchical structure, including spatial relations between objects and sub-objects. Second, in order to use the best criterion and threshold selection, the NN classifier had been used for features analysis. The class hierarchy was divided into 3levels, i.e. AOI/Non-AOI (L1), analysis level (L2) and top-most level (L3). Land use was classified into 8 classes based on the classification system, developed by Land Development Department, Thailand (e.g. urban/built-up land, agricultural area, range land, forest land and water). The classification was integrating not only spectral information, but also contextual and spatial relationships among image objects. A rule-based classifier has been used for the classification. The criterion of membership functions and the class descriptions were carried out from spectral information and spatial relation derived from image objects. Last, the classification result consists of 2 classes, non-village vs. residential. This study demonstrates that OBIA with topographic variables produces better classification results than OBIA with spectral information only. In the accuracy assessment, the result has been compared with reference ground truth points, including digital land use map, visual interpretation and TTA masks obtained from the pre-classification process. The overall accuracy achieved has reached approximately 70%, and Kappa index of agreement 0.64. OBIA technique is an effective tool for land use/land cover classification from VHRS data in rural areas aimed at land use planning, land use monitoring and ultimately for increasing the quality of life in rural societies.