Abstract
Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.
Highlights
Remote sensing data is widely used in different application fields nowadays
This study creates a hierarchical structure for land cover of Beibuwan Gulf, which is suitable for land cover extraction
In this study, some of the land cover classes are divided into subclasses or components in ontology, these classes can be better detected in computer
Summary
Remote sensing data is widely used in different application fields nowadays. For China, to achieve the annual National Geo-survey and monitoring, manual delineating land cover information from the large amount of remote sensing data is needed once a year, which covers the whole China area or even just a certain area, is an exhausting and challenging work. Object-oriented feature extraction that uses segmentation, spectral, texture, and shape information of the object solves these problems (Thomas, 2014) (Olive-Santos, 2014). It is widely used in image feature extraction in a period of time (Baraldi, 2012). Without the organization, storage and formal representation of information and knowledge for the feature, object-oriented method has shortages in its reusability and automation, which limits its usage by nonprofessional users or in large workload mission (Thomas, 2014) (Arvor, 2013). This study introduces an ontology-based framework and method for land cover extraction in the Beibuwan Gulf in Guangxi, China.
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