Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

Abstract

This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.

Assessment of DTMS Generated Using Different Wavelengths of Multispectral Lidar in Coastal and Forested Areas

“…The Cloud to Cloud (C2C) absolute distance method using cloud compare software was using the two filters for the three channels. The six output DTMs produced were compared with DTMs produced by its corresponding ENVI-LiDAR and the results are shown in figure (8,9,10). The blue color in the diagram means the data is nearest to the DTM created by ENVI-LiDAR while the red color shows that the deviation is in the maximum state.…”

“…This gives access to a variety of information about an area with different spectral properties of the objects. Table (1) shows the characteristic of Titan multispectral LiDAR and figure (1) shows the components of Titan multispectral lidar system which consist of a flight management system, an operator laptop, a digital camera, a laser scanner assembly, a Global Navigation Satellite System (GNSS), an Inertial Measurement Unit (IMU), and a control and data recording unit are fundamental parts of a multispectral lidar system (Ma et al, 2019).…”