Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method

Heng Luo,Haihong Zhu,Zhijun Zhang,Xi Kuai,Yu Liu,Lin Li

doi:10.3390/ijgi5030031

Heng Luo, Haihong Zhu + Show 4 more

Open Access

https://doi.org/10.3390/ijgi5030031

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Abstract

The rapid development and increasing availability of high-resolution satellite (HRS) images provides increased opportunities to monitor large scale land cover. However, inefficiency and excessive independence on expert knowledge limits the usage of HRS images on a large scale. As a knowledge organization and representation method, ontology can assist in improving the efficiency of automatic or semi-automatic land cover information extraction, especially for HRS images. This paper presents an ontology-based framework that was used to model the land cover extraction knowledge and interpret HRS remote sensing images at the regional level. The land cover ontology structure is explicitly defined, accounting for the spectral, textural, and shape features, and allowing for the automatic interpretation of the extracted results. With the help of regional prototypes for land cover class stored in Web Ontology Language (OWL) file, automated land cover extraction of the study area is then attempted. Experiments are conducted using ZY-3 (Ziyuan-3) imagery, which were acquired for the Jiangxia District, Wuhan, China, in the summers of 2012 and 2013.The experimental method provided good land cover extraction results as the overall accuracy reached 65.07%. Especially for bare surfaces, highways, ponds, and lakes, whose producer and user accuracies were both higher than 75%. The results highlight the capability of the ontology-based method to automatically extract land cover using ZY-3 HRS images.

Highlights

High-resolution satellite (HRS) data are widely used to monitor land cover by different application departments [1]
We focus on the use of a land cover ontology and regional prototype, which are used to perform the regional land cover extraction
The results show that ontology and prototype can be used to efficiently and effectively conduct automatic land cover extractions

Summary

Introduction

High-resolution satellite (HRS) data are widely used to monitor land cover by different application departments [1]. Remote sensing and geographic information system techniques are very useful for conducting researches like land cover change detection analysis and predicting the future scenario [4] As it was mentioned, the CGNCM mission which is proposed and implemented by National Administration of Surveying, Mapping, and Geoinformation, China requires annual land cover monitoring via HRS remote sensing data. Ontology can be used to provide expert knowledge and improve the satellite image extraction automation process by describing image segments based on spectral, textural, and shape features [1]. According to the FGIC ontology, remotely-sensed geographic information class data can be formalized based on the associated properties and some additional remotely sensed feature properties like spectral, texture, and so on. Creating a land cover class prototype may benefit the remote sensing knowledge organization and image extraction processes.

Data and Methods

Methods

Create Land Cover Class Prototype

Accuracy Assessment

Findings

Conclusions