Mapping Urban Land Cover of a Large Area Using Multiple Sensors Multiple Features

Jike Chen,Jocelyn Chanussot,Junshi Xia,Peijun Du,Changshan Wu

doi:10.3390/rs10060872

Abstract

Concerning the strengths and limitations of multispectral and airborne LiDAR data, the fusion of such datasets can compensate for the weakness of each other. This work have investigated the integration of multispectral and airborne LiDAR data for the land cover mapping of large urban area. Different LiDAR-derived features are involoved, including height, intensity, and multiple-return features. However, there is limited knowledge relating to the integration of multispectral and LiDAR data including three feature types for the classification task. Furthermore, a little contribution has been devoted to the relative importance of input features and the impact on the classification uncertainty by using multispectral and LiDAR. The key goal of this study is to explore the potenial improvement by using both multispectral and LiDAR data and to evaluate the importance and uncertainty of input features. Experimental results revealed that using the LiDAR-derived height features produced the lowest classification accuracy (83.17%). The addition of intensity information increased the map accuracy by 3.92 percentage points. The accuracy was further improved to 87.69% with the addition multiple-return features. A SPOT-5 image produced an overall classification accuracy of 86.51%. Combining spectral and spatial features increased the map accuracy by 6.03 percentage points. The best result (94.59%) was obtained by the combination of SPOT-5 and LiDAR data using all available input variables. Analysis of feature relevance demonstrated that the normalized digital surface model (nDSM) was the most beneficial feature in the classification of land cover. LiDAR-derived height features were more conducive to the classification of urban area as compared to LiDAR-derived intensity and multiple-return features. Selecting only 10 most important features can result in higher overall classification accuracy than all scenarios of input variables except the feature of entry scenario using all available input features. The variable importance varied a very large extent in the light of feature importance per land cover class. Results of classification uncertainty suggested that feature combination can tend to decrease classification uncertainty for different land cover classes, but there is no “one-feature-combination-fits-all” solution. The values of classification uncertainty exhibited significant differences between the land cover classes, and extremely low uncertainties were revealed for the water class. However, it should be noted that using all input variables resulted in relatively lower classification uncertainty values for most of the classes when compared to other input features scenarios.

Highlights

Detailed knowledge of the land cover types and their aerial distribution are essential components for the management and conservation of the land resource and are of critical importance to a series of studies such as climate change assessment and policy purpose [1,2,3].In recent decades, satellite remote sensing has exhibited its ability to achieve the land cover information with different temporal and spatial scales in the urban area
When the spatial features were combined for classification, all features together provided the maximum ability to separate different land cover classes, resulting in an overall map accuracy of 94.59%, and it is significant at the 95% level as compared to Scenario 6
We explored the use of multi-source remote sensing data to map urban land cover, with a particular focus on available input variables provided by airborne Light Detection and Ranging (LiDAR) and SPOT-5 data

Summary

Introduction

Detailed knowledge of the land cover types and their aerial distribution are essential components for the management and conservation of the land resource and are of critical importance to a series of studies such as climate change assessment and policy purpose [1,2,3].In recent decades, satellite remote sensing has exhibited its ability to achieve the land cover information with different temporal and spatial scales in the urban area. A multispectral satellite image can collect spectral information of land surfaces, and supply extra advantages to discriminate differences between urban land cover classes [4,5,6,7]. Even though many studies have successfully employed multispectral data for the classification of urban land cover, classification accuracy is more likely to be lower using spectral signature alone in the urban environment as compared to other environments such as forest environment. This is due to the fact that the urban environment possesses larger spectral and spatial heterogeneity of surface materials and the more complex pattern of land use [8]. In addition to devoting attention to the improving classification techniques, the development of input variables can be treated as an alternative way to improve the classification accuracy of a land cover map [7,9,10]

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