La-6501p Schematic

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With the emerging trend of big data and internet-of-things, sensors with compact size, low cost and robust performance are highly desirable. Spectral imaging and spectral LIDAR systems enable measurement of spectral and 3D information of the ambient environment. These systems have been widely applied in different areas including environmental monitoring, autonomous driving, biomedical imaging, biometric identification, archaeology and art conservation. In this review, modern applications of state-of-the-art spectral imaging and spectral LIDAR systems in the past decade have been summarized and presented. Furthermore, the progress in the development of compact spectral imaging and LIDAR sensing systems has also been reviewed. These systems are based on the nanophotonics technology. The most updated research works on subwavelength scale nanostructure-based functional devices for spectral imaging and optical frequency comb-based LIDAR sensing works have been reviewed. These compact systems will drive the translation of spectral imaging and LIDAR sensing from table-top toward portable solutions for consumer electronics applications. In addition, the future perspectives on nanophotonics-based spectral imaging and LIDAR sensing are also presented.

In addition to spectral information, to sense the 3-dimensional (3D) information of the object, light detection and ranging (LIDAR) technology provides an effective solution. LIDAR system primarily consists of a light source and a detector. By tracking the reflected signal from the object in ambient environment, the location and velocity information of the object can be obtained. The location information can then be used to reconstruct the 3D image of the object. LIDAR technology has been widely used in advanced driver-assistance systems (ADAS), autonomous driving and 3D sensing. It has become the eyes of robotics and cars to sense the ambient environment. The LIDAR technology has also been combined with the aforementioned spectral imaging technology to realize spectral LIDAR sensing systems [10], [11], [12], [13]. It can be used to determine the shape as well as the material composition of the objects, as different materials have unique reflectance in the optical spectrum. For example, the spectral reflectance of various plant species [14], gravel grain sizes [15], asphalt surfaces [16] are different and hence can be distinguished by using a multispectral imaging system.

Modern applications of spectral imaging and spectral LIDAR systems include environmental monitoring [3], [10], [11], [17], autonomous driving [18], [19], [20], biomedical imaging [2], [21], [22], biometric identification [23], [24], archaeology and art conservation [25], [26], as illustrated in Figure 1 left panel. These applications are enabled by the current state-of-the-art spectral imaging and spectral LIDAR systems. Also, there is a growing trend to make these systems more compact, lighter weight and with lower power consumption. The nanophotonics technology, with the capability to provide chip-scale high-performance functional devices, has been exploited to meet this emerging trend [27], [28], [29]. Comprehensive reviews on spectral imaging technologies and their applications have been reported before [2], [22], [25], [30], [31], [32]. However, the progress report on nanophotonics-based spectral imaging and LIDAR sensing systems is lacking. In this review, we summarized the recent research works on spectral imaging and spectral LIDAR systems, including the nanophotonics-based sensing systems. The modern applications of the current state-of-the-art spectral imaging and spectral LIDAR systems are presented in Section 2. A summary table categorizing the recent research works in the past decade based on application, sensing mechanism, sensor type and working wavelength is presented. Following that, in Section 3, the progress in recent development of nanophotonics-based spectral imaging and LIDAR sensing systems are reviewed and presented. A summary table has also been made based on the nanostructured material, sensing mechanism, application and wavelength. Finally, in Section 4, a summary of the review work and the outlook of future research directions in spectral imaging and LIDAR sensing systems are presented. The overview of the content has been illustrated in Figure 1.

Left panel: Modern applications chart of the state-of-the-art spectral imaging and spectral LIDAR sensing systems. Inset images: (top left) 2 dimensional (2D) multispectral images of urban area, adapted with permission from the study by Morsy et al. [11]. Licensed under a Creative Commons Attribution. (top and bottom right) A point cloud captured by line-scanning LIDAR system and schematic of LIDAR measurement setup, both adapted from the study by Taher [18] with permission. Copyright Josef Taher, Finnish Geospatial Research Institute FGI. (bottom left and middle) Schematic of multispectral facial recognition system setup and light source, both are adapted with permission from the study by Steiner et al. [23]. Licensed under a Creative Commons Attribution. Middle panel: Nanophotonics-based sensing systems. Inset images: (top and middle) Scanning electron microscopy (SEM) images of the fabricated color filters and optical image of color filters integrated with detector array, both are adapted with permission from the study by Shah et al. [33]. Licensed under a Creative Commons Attribution. (bottom) Schematic of dual-comb-based LIDAR system, adapted from the study by Trocha et al. [34]. Reprinted with permission from AAAS. Right panel: Outlook of the future development work for compact spectral imaging and LIDAR sensing systems.

Environment monitoring is the first application area that adopted spectral imaging solutions [3]. Over the past decade, with the advancements and wide applications of LIDAR systems, the multispectral LIDAR technology has been implemented for environment monitoring purpose as well. For example, in the study by Hopkinson et al. [10], the airborne LIDAR system (Teledyne Optech) is implemented for the characterization and classification of forest environment. In addition to the conventional 1064 nm single wavelength LIDAR system, 1550 and 532 nm wavelengths are also used for multispectral LIDAR sensing. Such sensing system provides improvements in land surface classification and vertical foliage partitioning. Furthermore, multispectral LIDAR has also been used for urban area classification, as reported in the studies by Morsy et al. and Huo et al. [11], [12]. In these reports, commercially available multispectral LIDAR sensors from Teledyne Optech and RIEGL Laser Measurement Systems, covering from visible wavelength (532 nm) to short wavelength infrared (SWIR) (1550 nm) are employed to generate multispectral LIDAR data. Different approaches are applied to classify areas (e.g., grass, roads, trees and buildings) within the urban area. In the study by Morsy et al. [11], the normalized difference vegetation indices (NDVI) computation is conducted for point-based classification of multispectral LIDAR data. In Figure 2(a), left and right panels show the 2D and 3D view of classified LIDAR points, respectively. The figures are based on NDVI computation using the recorded intensity at 532 and 1064 nm wavelength, which gives the overall accuracy of 92.7%.

(a) 2D and 3D multispectral images for urban area classification. These images are based on normalized difference vegetation indices (NDVI) computation using the recorded intensity at 532 and 1064 nm wavelength. (b) RGB and multispectral images of olive orchard and the imaging process flow for olive tree analysis. (a) and (b) are adapted with permission from the studies by Morsy et al. [11] and Jurado et al. [35], respectively. Both are licensed under a Creative Commons Attribution.

Alternative to multispectral LIDAR approach, in the study by Jurado et al. [35], a more cost-effective method, photogrammetry, is used to construct 3D images of olive trees. A high resolution camera is mounted on an unmanned aerial vehicle (UAV) to take multispectral images which are then reconstructed into 3D images. The multispectral images and RGB point clouds are fused to study an olive orchard. The methodology is illustrated in the scheme shown in Figure 2(b). It starts with the 3D reconstruction of both RGB and multispectral images as the first step. Following that, the reflectance maps are generated from the multispectral images (step two). These reflectance maps are used to enrich the 3D reconstructed images after alignment process, as shown in the third and fourth steps. After that, each olive tree has been segmented for morphological information extraction and temporal analysis. In addition to the airborne sensors mentioned above, spaceborne sensors have also been recently implemented for multispectral sensing. In the study by Torres et al. [36], sensors are mounted on a satellite to capture the multispectral images covering from visible to mid-infrared (MIR) wavelength range for earthquake vulnerability estimation.

One more point worth mentioning is that the multispectral images taken from environment can also be used for military and mineral mapping purposes. In military, the spectral imaging system provides information on 3D land cover of the battlefield [44]. The spectral information also facilitates the detection of target in varies degrees of camouflage [45]. Also, in mineral mapping, the spectral information enables identification of various mineral materials from the airborne hyperspectral images [5], [6], [7].

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