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

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.

Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data

“…Not only does its high spatial resolution allows for detailed image segmentation (Figure 4), the various structural, spectral and textural features derived from LiDAR data were also found to be the most important classification features overall. This is in line with a study by Chen, Du, Wu, et al [39], which concluded that height-related LiDAR features were more important compared to spectral features for urban land cover mapping. Whereas basic land cover classes could be readily differentiated using only LiDAR data, the added value of spectral data, and particularly of hyperspectral data, increased significantly when considering thematically more detailed urban (green) classes (Table 3).…”

“…Over time, many approaches have been suggested to allow for more detailed urban mapping. Firstly, fusion of spectral data with LiDAR (Light Detection and Ranging) data has been successfully applied in urban areas for land cover mapping [38][39][40][41][42], tree species classification [43], urban green mapping [44], detection of invasive shrub species [45] and tree health estimation [46] due to the high complementarity between spectral data and spatially very detailed structural information derived from 3D LiDAR data. Secondly, hierarchical (or stratified) classification approaches (i.e., classification done at multiple thematic levels, where each level is used as a constraint to map the next, more detailed level) have been shown to increase the mapping accuracy of detailed land cover classes [47][48][49].…”

Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models

“…The quantitative assessments of the four SMWF methods are shown in Table 2. The results were evaluated based on overall accuracy (OA), Kappa coefficient (KC), average producer's accuracy (APA), and average user's accuracy (AUA) [41,42]. Only mixed pixels in the flooding fraction images were included when calculating the evaluation indices.…”

Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

“…High-resolution remotely sensed imagery provides detailed spatial and structural information and thus offers new avenues for precise land cover classification over urban areas [3,4]. However, high spatial resolution does not indicate high precision for computer interpretation.…”