Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Abstract

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.

Pixel-Based Land Cover Classification by Fusing Hyperspectral And Lidar Data

“…For the classification, we used supervised classifier SVM and decision tree. SVM is the most popular supervised classifier for the classification of hyperspectral and LiDAR data (Dalponte et al, 2008;Luo et al, 2016;Gu et al, 2015;Wang and Glennie, 2015). However, in our case, the decision tree is performing better than SVM.…”

“…• We compare the performance of our feature combination with the feature combination proposed in (Luo et al, 2016). Our proposed additional features with the features used by Luo et al (2016) improves the classification accuracies.…”

“…Decision level fusion is the highest level of fusion which combines the classification results in classifier level. Luo et al (2016) performed land cover classification using hyperspectral and LiDAR data and classified seven different classes including buildings, road, water bodies, forests, grassland, cropland and barren land. They applied pixel based fusion (also called layer stacking) and Principle Component Analysis (PCA) on fused hyperspectral and LiDAR features.…”

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

“…Sinagra O. and Samsung L. (2014) also proved that using traditional classification methods of a variety of high resolution multispectral data combined with LiDAR helped in significantly improving the overall classification results of an urban environment in France [34]. At 1 m resolution, which is similar to the currently used datasets in the current project, Luo S. et al (2016) argued that using supervised classification method on high resolution aerial photos combined with LiDAR derived images resulted in a significantly improved accuracy compared to coarser resolution datasets [37]. In the current study, using MLC on high resolution multispectral NAIP aerial photos and LiDAR point return filtering achieved satisfactory average accuracy of about 95% when applied on three relatively different landscapes that included forested areas, agricultural fields (including tilled fields), built up environment and open land.…”

One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

“…For non-linearly separable problems, the kernel function is introduced to transform the original samples into a high dimensional feature space where the samples can be linearly separated. In this experiment, an RBF kernel was used since it has proved to be effective in LiDAR point cloud classification [10,36,50].…”