Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions.


  • Land cover information is an essential variable in main environmental problems of importance to the human-environmental sciences [1,2,3]

  • Supervised classifications were performed on four datasets with seven different spatial resolutions using the maximum likelihood classification (MLC) and support vector machine (SVM) classifiers

  • This study explored the potential for fusing airborne Light detection and ranging (LiDAR) and hyperspectral CASI data to classify land cover

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Land cover information is an essential variable in main environmental problems of importance to the human-environmental sciences [1,2,3]. Sensed data are most frequently used for land cover classification [6,9,10]. Many studies focusing on land cover classification using passive optical remotely sensed data have been conducted [11,12]. Accurate land cover classification using remotely sensed data remains a challenging task. Multiple studies have been performed to improve the land cover classification accuracy when using remotely sensed data, e.g., [13,14]. A limitation of this approach is that passive optical remote sensing data neglect the three-dimensional characteristics of ground objects and will reduce the land cover classification accuracy [5]. Optical remote sensing data can provide abundant spectral information of Earth surface and can be acquired at a relative low cost


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