scholarly journals TERRISCOPE: AN OPTICAL REMOTE SENSING RESEARCH PLATFORM USING AIRCRAFT AND UAS FOR THE CHARACTERIZATION OF CONTINENTAL SURFACES

2018 ◽  
Vol XLII-1 ◽  
pp. 65-70
Author(s):  
Y. Boucher ◽  
A. Amiez ◽  
P. Barillot ◽  
C. Chatelard ◽  
C. Coudrain ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
State Of The Art ◽  
Optical Measurements ◽  
Optical Remote Sensing ◽  
Airborne Laser ◽  
Laser Scanners ◽  
Research Platform ◽  
Infrared Cameras

<p><strong>Abstract.</strong> ONERA is developing TERRISCOPE, a new platform to characterize the environment and the continental surfaces by optical remote sensing using manned aircrafts and UAS (Unmanned Airborne System). The objective of TERRISCOPE is to make available to the scientific community combinations of optical measurements remotely sensed with the best level state-of-the-art sensors. Different kinds of sensors have already been acquired or are still being acquired: Hyperspectral sensors (0.5&amp;ndash;2.5<span class="thinspace"></span>&amp;mu;m range), visible high resolution cameras, multispectral infrared cameras and airborne laser scanners. Each sensor is declined in two versions: one high performance for manned aircrafts and one more compact for UAS. This paper describes the whole equipment, and presents the main characteristics and performances of the carriers, the sensors and the processing chain. Possible sensors combinations on airplanes and UAS are also presented, as well as preliminary results.</p>

Characterization of Near-Road Pollutant Gradients Using Path-Integrated Optical Remote Sensing

2008 ◽  
Vol 58 (7) ◽  
pp. 879-890 ◽  
Author(s):  
Eben D. Thoma ◽  
Richard C. Shores ◽  
Vlad Isakov ◽  
Richard W. Baldauf
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Integrated Optical

scholarly journals An Oil Well Dataset Derived from Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (6) ◽  
pp. 1132
Author(s):  
Zhibao Wang ◽  
Lu Bai ◽  
Guangfu Song ◽  
Jie Zhang ◽  
Jinhua Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Google Earth ◽  
Oil Wells ◽  
Oil Well ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Remote Sensing Images

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

scholarly journals A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1055-1060
Author(s):  
Y. Zheng ◽  
M. Guo ◽  
Q. Dai ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Image Filtering ◽  
Optical Remote Sensing ◽  
Low Resolution ◽  
Spectral Fidelity ◽  
Guided Image Filtering

The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details

scholarly journals Data-Dependent Acquisition and Database-Driven Efficient Peak Annotation for the Comprehensive Profiling and Characterization of the Multicomponents from Compound Xueshuantong Capsule by UHPLC/IM-QTOF-MS

Molecules ◽  
2019 ◽  
Vol 24 (19) ◽  
pp. 3431 ◽  
Author(s):  
Tiantian Zuo ◽  
Yuexin Qian ◽  
Chunxia Zhang ◽  
Yuxi Wei ◽  
Xiaoyan Wang ◽  
...  
Keyword(s):  
Ion Mobility ◽  
High Performance ◽  
State Of The Art ◽  
Reversed Phase ◽  
Bioactive Components ◽  
Chemical Complexity ◽  
Chinese Patent ◽  
Peak Annotation ◽  
Ophthalmic Disease

The state of the art ion mobility quadrupole time of flight (IM-QTOF) mass spectrometer coupled with ultra-high performance liquid chromatography (UHPLC) can offer four-dimensional information supporting the comprehensive multicomponent characterization of traditional Chinese medicine (TCM). Compound Xueshuantong Capsule (CXC) is a four-component Chinese patent medicine prescribed to treat ophthalmic disease and angina. However, research systematically elucidating its chemical composition is not available. An approach was established by integrating reversed-phase UHPLC separation, IM-QTOF-MS operating in both the negative and positive electrospray ionization modes, and a “Component Knockout” strategy. An in-house ginsenoside library and the incorporated TCM library of UNIFITM drove automated peak annotation. With the aid of 85 reference compounds, we could separate and characterize 230 components from CXC, including 155 ginsenosides, six astragalosides, 16 phenolic acids, 16 tanshinones, 13 flavonoids, six iridoids, ten phenylpropanoid, and eight others. Major components of CXC were from the monarch drug, Notoginseng Radix et Rhizoma. This study first clarifies the chemical complexity of CXC and the results obtained can assist to unveil the bioactive components and improve its quality control.

scholarly journals Shoreline Detection using Optical Remote Sensing: A Review

2019 ◽  
Vol 8 (2) ◽  
pp. 75 ◽  
Author(s):  
Seynabou Toure ◽  
Oumar Diop ◽  
Kidiyo Kpalma ◽  
Amadou Maiga
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Comparative Analysis ◽  
Coastal Erosion ◽  
State Of The Art ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Processing Methods ◽  
Key Concepts

With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted that with the development of remote sensing, shoreline detection is mainly achieved by image processing. Thus, it is important to evaluate the different image processing approaches used for shoreline detection. This paper presents a state of the art review on image processing methods used for shoreline detection in remote sensing. It starts with a review of different key concepts that can be used for shoreline detection. Then, the applied fundamental image processing methods are shown before a comparative analysis of these methods. A significant outcome of this study will provide practical insights into shoreline detection.

scholarly journals Apports et perspectives de la technologie LiDAR aéroporté pour la pratique forestière | Contributions and perspectives of airborne LiDAR technology for forestry

2008 ◽  
Vol 159 (2) ◽  
pp. 19-30
Author(s):  
Gilles Gachet ◽  
Pascal Junod
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Time Delay ◽  
Forest Structure ◽  
Laser Scanning ◽  
Airborne Lidar ◽  
Optical Remote Sensing ◽  
Sensing System ◽  
Airborne Laser ◽  
Reflected Signal

LiDAR is an optical remote sensing system that determines the distance to an object or to a surface by measuring the time delay between the transmission of a pulse and the detection of the reflected signal. A wide variety of applications arise from this technology which facilitate a better knowledge of land use. In forestry, airborne LiDAR systems provide, in addition to an accurate model of the relief, a 3D representation of the forest structure at various scales. This contribution presents the foundations of LiDAR technology and illustrates several forestry applications: underneath vegetation topography, canopy height and texture, vegetation delineation and stand delimitation. This contribution also outlines the potential of airborne laser scanning for forestry.

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