The present invention discloses, inter alia, a method for measuring and analyzing semi-transparent transient sources by remote sensing, comprising the steps of bore-sighting at least one spectrometer and at least one optic device selected from a group consisting of one or more spectrometers, one or more imagers, and at least one spectrometer and at least one imager; mounting at least one bore-sighted pair on at least one platform; and pointing simultaneously all platforms towards at least one field of view. The invention also discloses a platform for remote sensing of semi-transparent transient source comprising at least one first spectrometer in a first wavelength range; at least one second optic device selected from a group consisting of one or more spectrometers, one or more imagers, and at least one spectrometer and at least one imager; each of which is sensitive either in said first wavelength range or in any second wavelength range; at least one platform; wherein said at least one first spectrometer and said at least one second spectrometer are mounted on said platform and bore-sighted to observe the same or at least overlapping field of view.

Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a surface of a veneer sheet or other wood product. Based on the identified surface irregularities for a given wood product, one or more actions are taken with respect to wood product or production process to ensure the wood product is put to the most efficient, effective, and valuable use.

An inspection method, correction method, and inspection device that include measuring a first spatial position where laser light is emitted at a first region and measuring a first strike position where the inspection device is struck by the laser light in the first region, the measurements being performed by emitting the laser light at the first region of the inspection device; measuring a second spatial position where the laser light is emitted at a second region and measuring a second strike position where the inspection device is struck by the laser light in the second region, the measurements being performed by emitting the laser light at the second region of the inspection device; and comparing measurement results for the first spatial position and the second spatial position with measurement results for the first strike position and the second strike position.

Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based surface irregularity prediction models, are used to accurately identify surface irregularities on a surface of a wood product, such as a veneer sheet or ribbon, and provide irregularity prediction data for the wood product. Based on the irregularity prediction data for a given wood product, one or more actions are taken with respect to wood product or the production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Near InfraRed NIR technology, including NIR cameras and detectors, are used to detect irregularity in the surface of a wood product. Based on the detected irregularities at various locations in a given wood product, one or more actions are taken with respect to a production process used to produce the wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

A standoff chemical detection system that includes a source and detector are provided. The source includes: a controller, memory communicatively connected to the controller, optical sources each constructed to operate over different wavelength ranges, and a power supply. The controller controls the plurality of optical sources to emit respective infrared beams towards a target detection area in a sequential order. The detector includes: an image sensor and a controller that is communicatively connected to the image sensor. Memory and the notification device are also communicatively connected to the controller. The image sensor receives attenuated infrared beams emitted by the optical sources sequentially and at least partially attenuated by chemicals in the target detection area. The controller is constructed to calculate stimulus value signals from the recorded image data and determine whether a hazard chemical is located within the target detection area based on the calculated stimulus value signals.

Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a surface of a veneer sheet or other wood product. Based on the identified surface irregularities for a given wood product, one or more actions are taken with respect to wood product or production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based surface irregularity prediction models, are used to accurately identify surface irregularities on a surface of a wood product, such as a veneer sheet or ribbon, and provide irregularity prediction data for the wood product. Based on the irregularity prediction data for a given wood product, one or more actions are taken with respect to wood product or the production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Near InfraRed NIR technology, including NIR cameras and detectors, are used to detect irregularity in the surface of a wood product. Based on the detected irregularities at various locations in a given wood product, one or more actions are taken with respect to a production process used to produce the wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Described herein is a system and method for scanning a three-dimensional object using data from an infrared sensor. The data can be used during preprocessing, reconstructing and/or post processing of generation of a three-dimensional model. Data from an infrared sensor and data from a sensor (e.g., RGB sensor, a depth sensor, a camera, a scanner, a digital camera, a digital video camera, a web camera, depth sensor, etc.) can be utilized to generate a three-dimensional model of the three-dimensional object. For example, the data from the infrared sensor can be utilized to identify an item and to exclude the identified item from the generated three-dimensional model.