Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

Abstract

Feature extraction in cloud shadows is a difficult problem in the field of optical remote sensing. The key to solving this problem is to improve the accuracy of classification algorithms by fusing multi-source remotely sensed data. Hyperspectral data have rich spectral information but highly suffer from cloud shadows, whereas light detection and ranging (LiDAR) data can be acquired from beneath clouds to provide accurate height information. In this study, fused airborne LiDAR and hyperspectral data were used to extract urban objects in cloud shadows using the following steps: (1) a series of LiDAR and hyperspectral metrics were extracted and selected; (2) cloud shadows were extracted; (3) the new proposed approach was used by combining a pixel-based support vector machine (SVM) and object-based classifiers to extract urban objects in cloud shadows; (4) a pixel-based SVM classifier was used for the classification of the whole study area with the selected metrics; (5) a decision-fusion strategy was employed to get the final results for the whole study area; (6) accuracy assessment was conducted. Compared with the SVM classification results, the decision-fusion results of the combined SVM and object-based classifiers show that the overall classification accuracy is improved by 5.00% (from 87.30% to 92.30%). The experimental results confirm that the proposed method is very effective for urban object extraction in cloud shadows and thus improve urban applications such as urban green land management, land use analysis, and impervious surface assessment.