Spectral Sensor(光谱传感器)研究综述
Spectral Sensor 光谱传感器 - To obtain more abundant target information, the panchromatic and multispectral sensor (PMS) of the GaoFen-4 (GF-4) satellite utilizes five different integration times. [1] We present a spectral sensor based on an integrated array of resonant-cavity-enhanced photodetectors operating in the near-infrared. [2] Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). [3] Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status. [4] The purpose of this study is to investigate the potential of satellite SAR and multispectral sensors in the detection of biogenic oil films near aquaculture farms. [5] A UAV-mounted multispectral sensor was flown over the trial 14 days after the herbicide treatments. [6] Hyperspectral sensors collect spectral data in numerous adjacent spectral bands, which are usually redundant. [7] The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. [8] The spectra contain data regarding all phases of the soil components (mineral, organic, liquid and gaseous), using a large variety of equipment with hyperspectral and multispectral sensors (from the air–drones, satellites; from the soil-scanning equipment, mounted on the tractors–YARA sensor, etc. [9] The proposed algorithm is used to extract six water bodies with different complex texture backgrounds from multispectral sensors. [10] Novelty: The present work highlights the usefulness of the moderate resolution multispectral image in mapping the Kimberlite pipes in semiarid region, in absence of a hyperspectral sensor. [11] In our experiments with different images our accelerator can process the hyperspectral images at the same speed at which they are generated by the hyperspectral sensors. [12] We present a fuzzy logic approach allowing the identification of minerals from re ectance spectra acquired by hyperspectral sensors in the VNIR and SWIR ranges. [13] Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. [14] The automatic vegetation eco-meteorological observation instruments, whichi are composed of image sensor (digital camera), multispectral sensor, laser altimeter, point cloud laser radar and sound sensor, have been installed in the sites. [15] Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). [16] Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. [17] Hyperspectral images such as the Earth Observer-1 (EO-1) provides an efficient method of mapping surface mineralogy because it can measures the energy in narrower bands compared with multispectral sensors. [18] The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. [19] To understand this loss, in this study, we demonstrate the potential of a multi-rotor and a multispectral sensor for spatial assessment and monitoring for seagrass meadows in Waitemata Harbour, Auckland, New Zealand. [20] Experiments are also carried out on three real hyperspectral images acquired by two hyperspectral sensors. [21] Here, we propose a machine learning approach based on the Google Earth Engine (GEE) platform to analyze simultaneously the images acquired by the synthetic aperture radar (SAR) sensor, on board of Sentinel-1 mission, and by optical and multispectral sensors of Landsat-8 missions and Multi-Spectral Imager (MSI), on board of Sentinel-2 mission. [22] During the past 20 years, technologies including satellites, manned and unmanned aircraft, spectral sensors, information systems, and autonomous field equipment, have been used to detect pests and apply control measures site-specifically. [23] In the Philippines there are currently no non-invasive procedures to measure uric acid in the blood, the objective of this study is to implement the use of Spectral sensor and Spectroscopy as a means of measuring uric acid non-invasively. [24] In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. [25] In this study, the images of Sentinel-2B multispectral sensor are used to map minerals associated with rare earth elements (REE) in an attempt to discover new potential targets for REE deposits in the Schiel Alkaline Complex of South Africa. [26] A hyperspectral sensor was used to capture ultra-high spatial and spectral information pertaining to redwood trees with no damage, those that have been recently attacked by bears, and those with old bear damage. [27] Promoting the spatial resolution of hyperspectral sensors is expected to improve computer vision tasks. [28] This study applies lunar calibration to a multispectral sensor, Ocean Observation Camera (OOC), on board a microsatellite named Rapid International Scientific Experiment Satellite. [29] Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. [30] The GS-based methods were implemented to calibrate two Chinese large-view-angle sensors: the Gaofen-1 first wide-field-of-view (WFV1) camera and Gaofen-4 panchromatic multispectral sensor (PMS) with Landsat-8/Operational Land Imager (OLI) as references. [31] Modern hyperspectral sensors can capture a portion of the electromagnetic spectrum from the visible region (0. [32] Here, we report an ultrasensitive hyperspectral sensor (HyperSENSE) based on hafnium nanoparticles (HfNPs) for specific detection of COVID-19 causative virus, SARS-CoV-2. [33] The miniaturisation of spectral sensors is essential to expanding their application beyond dedicated stations in industrial settings and analytical labs, into the hands of non-specialists working on-site and eventually to consumers. [34] The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. [35] Common hyperspectral sensors divide the sensed optical bandwidth into as many as 270 different channels. [36] This method aims to build a model relationship (called prediction model) between feature variables acquired by spectral sensors and components to be measured. [37] Alternatively, hyperspectral sensors have potential to determine physiological status of the crops. [38] HISUI, the Japanese hyperspectral sensor, was launched on December 6, 2019 and the first image was taken on September 4, 2020. [39]为了获得更丰富的目标信息，高分四号（GF-4）卫星的全色多光谱传感器（PMS）采用了五种不同的积分时间。 [1] 我们提出了一种基于在近红外中工作的谐振腔增强型光电探测器的集成阵列的光谱传感器。 [2] SPECIM IQ 400–1000 nm 高光谱传感器（芬兰奥卢）捕获每根葡萄藤的图像。 [3] 综合起来，这些发现表明，高光谱传感器可用于快速、无损地估计叶片代谢物，从而总结植物的生理状态。 [4] 本研究的目的是研究卫星 SAR 和多光谱传感器在检测水产养殖场附近的生物油膜方面的潜力。 [5] 在除草剂处理 14 天后，一个安装在无人机上的多光谱传感器在试验中飞行。 [6] 高光谱传感器收集许多相邻光谱带中的光谱数据，这些光谱带通常是多余的。 [7] 上世纪 80 年代初，人们强调了对更密集光谱信息的需求，第一个基于卫星的高光谱传感器 AVIRIS 开始提供数据，允许提取有关材料成分的信息和精确的表面覆盖信息。 [8] 光谱包含有关土壤成分所有阶段（矿物、有机、液体和气体）的数据，使用具有高光谱和多光谱传感器的各种设备（来自无人机、卫星；来自安装在拖拉机——YARA传感器等。 [9] 所提出的算法用于从多光谱传感器中提取具有不同复杂纹理背景的六个水体。 [10] 新颖性：目前的工作强调了在没有高光谱传感器的情况下，中等分辨率多光谱图像在绘制半干旱地区金伯利岩管道的有用性。 [11] 在我们对不同图像的实验中，我们的加速器可以以与高光谱传感器生成它们的速度相同的速度处理高光谱图像。 [12] 我们提出了一种模糊逻辑方法，允许从 VNIR 和 SWIR 范围内的高光谱传感器获取的反射光谱中识别矿物。 [13] 大气校正 (AC) 算法专门设计用于处理这些影响，但受到高光谱传感器获得的数百个窄光谱带的挑战。 [14] 现场安装了由图像传感器（数码相机）、多光谱传感器、激光测高仪、点云激光雷达和声音传感器组成的植被生态气象自动观测仪器。 [15] 携带低成本和轻型多光谱传感器的当代无人机系统 (UAS) 可提供高空间分辨率图像 (<10 cm)。 [16] 高分四号（GF-4）是一颗地球静止卫星，搭载全色多光谱传感器（PMS），在观测大气气溶胶方面具有巨大潜力。 [17] Earth Observer-1 (EO-1) 等高光谱图像提供了一种绘制地表矿物学绘图的有效方法，因为与多光谱传感器相比，它可以测量更窄波段的能量。 [18] 该研究旨在利用 Sentinel-2 多光谱传感器，使用集成机器学习分类器检测莱基皮亚县异构 ASAL 中的仙人掌。 [19] 为了了解这种损失，在这项研究中，我们展示了多旋翼和多光谱传感器在新西兰奥克兰怀特玛塔港的海草草地空间评估和监测方面的潜力。 [20] 实验还对两个高光谱传感器获取的三个真实高光谱图像进行了实验。 [21] 在这里，我们提出了一种基于谷歌地球引擎（GEE）平台的机器学习方法，以同时分析合成孔径雷达（SAR）传感器、Sentinel-1 任务以及 Landsat 的光学和多光谱传感器获取的图像-8 任务和多光谱成像仪 (MSI)，在 Sentinel-2 任务上。 [22] 在过去的 20 年中，包括卫星、有人驾驶和无人驾驶飞机、光谱传感器、信息系统和自主现场设备在内的技术已被用于检测害虫并针对特定地点采取控制措施。 [23] 在菲律宾，目前没有非侵入性程序来测量血液中的尿酸，本研究的目的是实施使用光谱传感器和光谱作为非侵入性测量尿酸的手段。 [24] 在本文中，我们介绍了一种低成本的基于智能手机的高光谱成像系统，该系统可以将标准智能手机相机转换为可见波长的高光谱传感器，用于 ca。 [25] 在这项研究中，Sentinel-2B 多光谱传感器的图像用于绘制与稀土元素 (REE) 相关的矿物图，试图在南非 Schiel 碱性复合体中发现 REE 矿床的新潜在目标。 [26] 高光谱传感器用于捕获与没有损坏的红杉树、最近被熊袭击的红杉树以及受老熊损坏的红杉树有关的超高空间和光谱信息。 [27] 提高高光谱传感器的空间分辨率有望改善计算机视觉任务。 [28] 这项研究将月球校准应用于多光谱传感器，即海洋观测相机 (OOC)，该传感器位于一颗名为快速国际科学实验卫星的微型卫星上。 [29] 无人机系统 (UAS) 搭载商业销售的配备阳光传感器的多光谱传感器，例如 Parrot Sequoia，能够以高空间分辨率绘制植被图，并在规划数据收集方面具有很大的灵活性。 [30] 实施基于 GS 的方法来校准两个中国大视角传感器：高分一号首个宽视场 (WFV1) 相机和高分四号全色多光谱传感器 (PMS) 与 Landsat-8/Operational Land Imager (OLI) 作为参考。 [31] 现代高光谱传感器可以从可见区域（0. [32] 在这里，我们报告了一种基于铪纳米粒子 (HfNPs) 的超灵敏高光谱传感器 (HyperSENSE)，用于特异性检测 COVID-19 致病病毒 SARS-CoV-2。 [33] 光谱传感器的小型化对于将其应用扩展到工业环境和分析实验室的专用站之外，进入现场工作的非专业人员手中并最终进入消费者手中至关重要。 [34] 来自多光谱传感器的时间序列 NDVI 数据是在整个生长季节的五个时间点获得的，用于使用 UAV-HTPP 的 1,752 个不同的玉米种质。 [35] 常见的高光谱传感器将感测到的光学带宽分成多达 270 个不同的通道。 [36] 该方法旨在建立光谱传感器获取的特征变量与待测元件之间的模型关系（称为预测模型）。 [37] 或者，高光谱传感器有可能确定作物的生理状态。 [38] 日本高光谱传感器 HISUI 于 2019 年 12 月 6 日推出，第一张图像于 2020 年 9 月 4 日拍摄。 [39]
unmanned aerial vehicle 无人驾驶的航空机
In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. [1] Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. [2] Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. [3] 26 VIs and 5 spectral bands obtained from a red-edge multispectral sensor mounted on Unmanned Aerial Vehicles (UAVs) were analyzed to develop machine learning models for percent leaf N estimation of corn. [4] Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. [5] Unmanned aerial vehicles with hyperspectral sensors present new opportunities for acquiring imagery in overcast conditions. [6] A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. [7] For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. [8] An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. [9] In this study, aerial images acquired by a quadcopter unmanned aerial vehicle (UAV) equipped with a multispectral sensor were used to estimate plant nitrogen content at vegetative phase of rice crops. [10] The continuous measurement of atmospheric CH4 for 2 days validated the feasibility and robustness of our laser spectrometer, providing a promising laser spectral sensor for deploying in unmanned aerial vehicles or mobile robots. [11] Hyperspectral sensors that are mounted in unmanned aerial vehicles (UAVs) offer many benefits for different remote sensing applications by combining the capacity of acquiring a high amount of information that allows for distinguishing or identifying different materials, and the flexibility of the UAVs for planning different kind of flying missions. [12] A multispectral sensor installed in an unmanned aerial vehicle (UAV) was used to obtain the VIs, and the application rates evaluated were 40, 70, 100, and 130 L ha–1. [13] Two studies from 2016 to 2017 were initiated with the intent of identifying the spectral reflectance properties of Italian ryegrass and winter wheat using an unmanned aerial vehicle (UAV) equipped with a 5-band multispectral sensor. [14] This study developed a method that incorporates capabilities of Unmanned Aerial Vehicles (UAVs) equipped with a multispectral sensor in N monitoring specifically in rice crops, a major agricultural product in the Philippines. [15] In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. [16]在我们的研究中，使用配备有高光谱传感器的无人机 (UAV) 来获取野外尺度的高光谱图像。 [1] 这种严格的现场采样练习繁琐、繁琐，并且在具有挑战性的地形上通常不切实际，这是安装在无人机（UAV-高光谱系统）上的可编程高光谱传感器的一个限制因素，需要在绘制新环境时预先选择最佳波段具有未知光谱的新目标类别。 [2] nan [3] 分析了从安装在无人驾驶飞行器 (UAV) 上的红边多光谱传感器获得的 26 个 VI 和 5 个光谱带，以开发用于估计玉米叶片 N 百分比的机器学习模型。 [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12]