An Improved Urban Mapping Strategy Based on Collaborative Processing of Optical and SAR Remotely Sensed Data

Ruimei Han,Hanwei Zhang,Pei Liu,Leiku Yang,Han Wang,Chao Ma

doi:10.1155/2017/9361592

Abstract

Potential of combining SAR and optical remotely sensed data for rapid urban mapping is highlight. Two groups of optical and SAR remotely sensed data are selected to evaluate the strategy. Outputs are verified and analyzed from 3 aspects. The single class and merged map accuracy are evaluated; the proposed method is compared with 2 mature algorithms; the selected classifiers are applied to 7 different fusion algorithms to make further comprehension. The outcomes illustrate the potential of synergic optical and SAR data for monitoring urbanization status and demonstrate that the proposed SAR/optical information synergy method improved the capabilities of urban mapping compared with separately using SAR and optical data. The results demonstrate that the proposed method can map built-up area, water body, and vegetation at accuracy of 99.31%, 91.92%, and 91.72%, respectively. These results are much better than when solo optical or SAR data was selected and better than classification results based on mature fusion methods. The main contributions of this article are as follows: the proposal of a rapid urban mapping strategy based on integration of optical and SAR data and the verifying and analysis of potential of synergic optical and SAR data for rapid urban mapping.

Highlights

With the development of aerospace technology and remote sensing technique, more and more earth observation data archives are becoming available which increases the possibility of joint use of optical and SAR data, as well as different sensors at a regional or global level for urban monitoring
The first three steps can be summarized as rapid urban mapping stage, detailed as follows: human settlement is extracted from passive SAR data with method based on Gray-Level Cooccurrence Matrix (GLCM) and Local Indicators of Spatial Association (LISA) unsupervised method in the first step; vegetation and water body are generated using Normalized Difference Vegetation Index (NDVI) and Modified Normal Differential Water Index (MNDWI) quantitative indexes, respectively; these primary results are merged with a decision fusion algorithm to deal with omission/commission pixels and generate final urban cover map
The accuracy of merged results is compared with accuracy of vegetation and water body types extracted from threshold NDVI and MNDWI and human settlement extracted using LISA and GLCM method

Summary

Introduction

With the development of aerospace technology and remote sensing technique, more and more earth observation data archives are becoming available which increases the possibility of joint use of optical and SAR data, as well as different sensors at a regional or global level for urban monitoring. As a matter of fact, in the last decade, there have been a number of different approaches for urban mapping [1,2,3]. Some of these literatures focus on urban mapping using only optical remotely sensed data for many years in several parts of the world [4,5,6,7], because the optical remotely sensed data which is richer in spectral information would be on behalf of surface reflective and emissive spectrum. The purpose of this study is to explore a convenient way to obtain urban cover types by cooperating with results extracted from active and passive remotely sensed data

Objectives

Methods

Results

Discussion

Conclusion