Feasibility Study on Hyperspectral LiDAR for Ancient Huizhou-Style Architecture Preservation

Abstract

Huizhou-style ancient architecture was one of the most important genres of architectural heritage in China. The architecture employed bricks, woods, and stones as raw materials, and timber frames were significant structures. Due to the drawback that the timbers were vulnerable to moisture and atmospheric agents, ancient timber buildings needed frequent protective interventions to maintain its good condition. Such interventions unavoidably disrupted the consistency between the original timber components. Besides this, the modifications brought about difficulty in correctly analysing and judging the state of existing ancient buildings, which, in current preservation practices, mainly rely on the expertise of skilled craftsmen to classify wood species and to identify the building-age of the timber components. Therefore, the industry and the research community urgently need a technique to rapidly and accurately classify wood materials and to discriminate building-age. In the paper, we designed an eye-safe 81-channel hyperspectral LiDAR (HSL) to tackle these issues. The HSL used an acousto-optic tunable filter (AOTF) as a spectral bandpass filter, offering the HSL measurements with 5 nm spectral resolution. Based on the HSL measurements, we analysed the relationship between the surface and cross-section spectral profiles of timber components from different ancient architectures built in the early Qing dynasty (~300 years), late Qing dynasty (~100 years), and nowadays, and confirmed the feasibility of using surface spectra of timber components for classification purpose. We classified building-ages and wood species with multiple Naive Bayes (NB) and support vector machine (SVM) classifiers by the surface spectra of timber components; this also unveiled the possibility of classifying gnawed timber components from its spectra for the first time. The encouraging experimental results supported that the AOTF-HSL is feasible for historic timber building preservation.

Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)

“…HSL hardware prototype in full band was designed at the Finnish Geodetic Institute, which was used for vegetation remote sensing in 2012 [13]. Recently, various applications of HSL have been reported, such as coal/rock classification [15], vegetation detection [14], vegetation index extraction [16], architecture preservation [17], and ore classification [18].…”

Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging

“…(iv) The valuable wavelengths for the identification of tumors could be selected from NIR-HSI data using resected specimens. In this study, OTN-NIR MSI was investigated using commercially available filters via straightforward construction; however, spectral images with high wavelength resolution could be obtained by adopting an acoustic tunable filter (AOTF) and a liquid crystal tunable filter to select the wavelength before imaging or light output [21,29,30]. In particular, a combination of an AOTF and a supercontinuum light source is promising because high power output and fast wavelength switching can be obtained.…”

An Investigation of Spectral Band Selection for Hyperspectral LiDAR Technique

“…We have designed an AOTF-HSL for experiments in a controlled laboratory environment, which can acquire proper range accuracy through the whole spectrum. The HSL range is beyond 15 m [24,27], proving that its effective range satisfies practical applications. If the hardware is further simplified, the device will meet the market demand as the selected distance of tens of meters offers a feasible, effective radius for practical survey and records in future with an on-site terrestrial scanning mode.…”

“…The reflectance is the calibrated echo intensity, which is a prerequisite for many quantitative applications, and it has become an important research topic recently [20][21][22][23][24]. In this paper, we conducted LiDAR calibration assisted with a white reflectance standard board with 70% reflectivity [19,24,27]. Figure 2 shows the spectral profiles of the echo intensity and reflectance maximum from six different samples in Dataset 4.…”