Optimizing Support Vector Machine and Ensemble Trees Using Taguchi Method for Road Extraction from LiDAR Data

Vehicle extraction becomes possible as the high-performance airborne light detection and ranging (LiDAR) systems can offer very dense and accurate point cloud, which means the sophisticated objects can be recorded in detail, combined with color information from airborne image, hyperspectral and intensity. However, few studies have investigated in extracting vehicles from LiDAR data only, especially when its quality is low, which is the main difficulty for most LiDAR applications. In this paper, a hybrid approach has been proposed to extract vehicles from low-quality LiDAR data. In order to extract vehicle from low-resolution LiDAR data, a robust one-class support vector machine-minimum covariance determinant (OCSVM-MCD) is proposed based on a multivariate dispersion estimator and weighted strategy. Firstly, the three-dimensional (3D) point dataset is classified into nonterrain and terrain points with progressive morphological filter with a slight improvement. Secondly, nonterrain points are segmented by clustering technique and missing blobs are searched from terrain points. Then, the vehicles are extracted from clustering and searching results by OCSVM-MCD, and a hybrid principle is put forward to improve the extraction result at last. The proposed method has been evaluated with two benchmark datasets from ISPRS, and proved that by the method, most vehicles can be extracted from low-quality LiDAR data with an encouraging result.

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Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

Forest Systems ◽

10.5424/fs/2018273-13686 ◽

2018 ◽

Vol 27 (3) ◽

pp. eSC03 ◽

Cited By ~ 2

Author(s):

Miguel Garcia-Hidalgo ◽

Ángela Blázquez-Casado ◽

Beatriz Águeda ◽

Francisco Rodriguez

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Digital Terrain Model ◽

Image Data ◽

Tree Height ◽

Support Vector ◽

Lidar Data ◽

Support Vector Machine Algorithm ◽

Terrain Model ◽

Sentinel 2

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types. Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out. Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable. Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable. SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD). SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively. SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables. Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest.

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