Using near-infrared hyperspectral images on subalpine fir board. Part 1: Moisture content estimation

Abstract

In this study, moisture content (MC) images of subalpine fir (abies lasiocarpa Hook) boards were derived from near-infrared hyperspectral images in the 947-1637 nm range. One hundred and seven cubic samples with the size of 4 cm were prepared from 14 boards. All samples were dried to various MCs during several steps until being completely dried. Hyperspectral images and weight measurements were acquired over each sample at each drying step. The samples have MC ranging from 1% to 137% (dry basis). The images were first calibrated into reflectance. Then, bad pixels were found and replaced by a corrected value using a median filter. A modified version of the boxplot method was used to find abnormal spectra that were then removed. The remaining spectra were converted into absorbance spectra. They were then split into a calibration and a validation data-set according to the boards they were extracted from to build and validate a partial least squares (PLS) regression model between the near-infrared absorbance spectra and the measured MCs. The PLS model was applied first to the sample images, then to the whole board images in order to produce 2D images of MC.

Hyperspectral imaging and chemometrics reveal wood acetylation on different spatial scales

“…Chemical imaging is a promising tool for determining the chemical properties of wood. Hyperspectral NIR images have previously been used for evaluating the chemical composition of wood [10,11], identifying different wood species [12], predicting wood moisture content and physical properties [13,14], and visualizing the level of wood extractives [15]. The use of NIR images has also been reported in predicting vapor-phase acetylation of wood [16].…”

Sensors in the Forest Products Industry

“…NIRS-based hyperspectral images are used to estimate moisture content and density distributions in subalpine fir board (Haddadi et al 2015a, b). Timeof-flight NIRS is used to measure variations in air-dry wood density (Hans et al 2015b).…”

Determination of log moisture content using ground penetrating radar (GPR). Part 1. Partial least squares (PLS) method

“…Recent methods are increasingly focusing on sensors using electromagnetic (EM) radiations of various wavelengths ( Brashaw et al 2009 ). For example, wood MC was recently successfully determined based on X-ray imaging ( Tanaka et al 2009 ;Wei et al 2011 ), near-infrared (NIR) spectroscopy ( Karttunen et al 2008 ;Watanabe et al 2011 ;Hans et al 2013 ;Leblon et al 2013 ;Tsuchikawa and Schwanninger 2013 ), hyperspectral imaging ( Mora et al 2011 ; T ö rm ä nen and M ä kynen 2011 ; Kobori et al 2013 ;Haddadi et al 2014 ), NIR time-resolved spectroscopy ( D ' Andrea et al 2008( D ' Andrea et al , 2009, terahertz time domain spectroscopy ( Inagaki et al 2014 ), ground penetrating radar (GPR) ( Paz 2008( Paz , 2010Paz et al 2008 ;Rodr í guez-Abad et al 2010aHans et al 2014 ), microwave scanning ( Bucur 2003 ;Lundgren et al 2006 ;Schajer and Orhan 2006 ), and nuclear magnetic resonance ( Hartley et al 1994 ;Merela et al 2009 ;MacMillan et al 2011 ;Telkki et al 2013 ;Lamason et al 2014 ).…”