Abstract
Full-Waveform (FW) Light Detection and Ranging (LiDAR) systems record the complete waveforms of backscattered laser signals, thus providing greater potential for extracting additional features and deriving physical properties from reflected laser signals. This study explores the feasibility of extracting volumetric texture features from airborne FW LiDAR point cloud data along with echo-based LiDAR features to improve land-cover classification. A second derivative algorithm is used to detect signal echoes and extract single- and multi-echo features from FW LiDAR data derived from Gaussian fitting function. The dense point clouds are further regularized to construct a data cube for volumetric texture extractions using 3D-GLCM (Gray Level Co-occurrence Matrix) and Gray Level Co-occurrence Tensor Field (GLCTF) algorithms coupled with second and third order texture descriptors. Different feature combinations of traditional and echo-based LiDAR features and texture measures are collected for supervised land-cover classification using a Random Forests classifier. The experimental results indicate that the echo-based features may be useful for distinguishing general land-cover types with acceptable accuracy but may not be adequate for detailed classifications, such as discriminating different vegetation cover types. Incorporating volumetric texture features can improve the classification of relatively more detailed land-cover types with an approximate 10 and 14% increase in the overall accuracy and Kappa coefficient, respectively.
Highlights
Airborne laser scanning (ALS) system is an active remote sensing technique that emits pulses and receives their responses to measure attitude angles and distances between the sensor and targets
A three-phase scenario with three test cases was adopted to demonstrate the effectiveness of volumetric texture features for improving FW Light Detection and Ranging (LiDAR) point cloud land-cover classification
To understand the effectiveness of the selected classifier (RF), it was compared with the Naive Bayes (NB) algorithm using test case 1 as an example and with traditional LiDAR features and single- and multiecho features derived from second-derivative echo detection and Gaussian fitting
Summary
Airborne laser scanning (ALS) system ( known as airborne Light Detection and Ranging, LiDAR) is an active remote sensing technique that emits pulses and receives their responses to measure attitude angles and distances between the sensor and targets. The target’s coordinates can be computed using direct geo-referencing theory. The LiDAR outcome is referred to as point clouds consisting of many discrete points. The pulse response with the passing of time is called a waveform. Most ALS systems record part of the waveform and the number of records depends on the instrument, e.g., one echo derived from the first return, two echoes composed of first and last return, or six echoes determined by echo detection in a waveform.
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