The water color simulator WASI: an integrating software tool for analysis and simulation of optical in situ spectra*1

2004 ◽  
Author(s):  
P GEGE
Keyword(s):  
Software Tool ◽  
Water Color ◽  
In Situ Spectra

Study on space weathering of Chang′e-4 landing site by in situ spectra

2020 ◽  
Vol 49 (5) ◽  
pp. 20190460
Author(s):  
王振超 Zhenchao Wang ◽  
柳稼航 Jiahang Liu ◽  
盛庆红 Qinghong Sheng ◽  
吴昀昭 Yunzhao Wu
Keyword(s):  
Landing Site ◽  
Space Weathering ◽  
In Situ Spectra

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

scholarly journals ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images

Animals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 58 ◽  
Author(s):  
Greg Falzon ◽  
Christopher Lawson ◽  
Ka-Wai Cheung ◽  
Karl Vernes ◽  
Guy A. Ballard ◽  
...  
Keyword(s):  
Software Tool ◽  
Camera Trap ◽  
Cloud Services ◽  
Third Party ◽  
End User ◽  
Automated Identification ◽  
Data Control ◽  
Visible Object ◽  
Privacy Issues

We present ClassifyMe a software tool for the automated identification of animal species from camera trap images. ClassifyMe is intended to be used by ecologists both in the field and in the office. Users can download a pre-trained model specific to their location of interest and then upload the images from a camera trap to a laptop or workstation. ClassifyMe will identify animals and other objects (e.g., vehicles) in images, provide a report file with the most likely species detections, and automatically sort the images into sub-folders corresponding to these species categories. False Triggers (no visible object present) will also be filtered and sorted. Importantly, the ClassifyMe software operates on the user’s local machine (own laptop or workstation)—not via internet connection. This allows users access to state-of-the-art camera trap computer vision software in situ, rather than only in the office. The software also incurs minimal cost on the end-user as there is no need for expensive data uploads to cloud services. Furthermore, processing the images locally on the users’ end-device allows them data control and resolves privacy issues surrounding transfer and third-party access to users’ datasets.

scholarly journals MULTI-CHANNEL CONTROL SYSTEM FOR IN-SITU LABORATORY LOADING DEVICES

2018 ◽  
Vol 18 ◽  
pp. 15 ◽  
Author(s):  
Václav Rada ◽  
Tomáš Fíla ◽  
Petr Zlámal ◽  
Daniel Kytýř ◽  
Petr Koudelka
Keyword(s):  
Open Source ◽  
Software Tool ◽  
Cnc Machine ◽  
Control Software ◽  
Wide Range ◽  
Hardware Abstraction Layer ◽  
Channel Control ◽  
Python Programming ◽  
Loading Devices

In recent years, open-source applications have replaced proprietary software in many fields. Especially open-source software tools based on Linux operating system have wide range of utilization. In terms of CNC solutions, an open-source system LinuxCNC can be used. However, the LinuxCNC control software and the graphical user interface (GUI) could be developed only on top of Hardware Abstraction Layer. Nevertheless, the LinuxCNC community provided Python Interface, which allows for controlling CNC machine using Python programming language, therefore whole control software can be developed in Python. The paper focuses on a development of a multi-process control software mainly for in-house developed loading devices operated at our institute. The software tool is based on the LinuxCNC Python Interface and Qt framework, which gives the software an ability to be modular and effectively adapted for various devices.

scholarly journals Design and Experiments of a Water Color Remote Sensing-Oriented Unmanned Surface Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2183
Author(s):  
Yong Li ◽  
Liqiao Tian ◽  
Wenkai Li ◽  
Jian Li ◽  
Anna Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Obstacle Avoidance ◽  
Ocean Color ◽  
Water Color ◽  
Unmanned Surface Vehicle ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
In Situ Observations

Integrated and intelligent in situ observations are important for the remote sensing monitoring of dynamic water environments. To meet the field investigation requirements of ocean color remote sensing, we developed a water color remote sensing-oriented unmanned surface vehicle (WC-USV), which consisted of an unmanned surface vehicle platform with ground control station, data acquisition, and transmission modules. The WC-USV was designed with functions, such as remote controlling, status monitoring, automatic obstacle avoidance, and water and meteorological parameter measurement acquisition, transmission, and processing. The key data acquisition module consisted of four parts: A floating optical buoy (FOBY) for collecting remote sensing reflectance ( R r s ) via the skylight-blocked approach; a water sample autocollection system that can collect 12 1-L bottles for analysis in the laboratory; a water quality measurement system for obtaining water parameters, including Chlorophyll-a (Chl-a), turbidity, and water temperature, among others; and meteorological sensors for measuring wind speed and direction, air pressure, temperature, and humidity. Field experiments were conducted to validate the performance of the WC-USV on 23–28 March 2018 in the Honghu Lake, which is the seventh largest freshwater lake in China. The tests proved the following: (1) The WC-USV performed well in terms of autonomous navigation and obstacle avoidance; (2) the mounted FOBY-derived R r s showed good precision in terms of the quality assurance score (QAS), which was higher than 0.98; (3) the Chl-a and suspended matters (SPM) as ocean color parameters measured by the WC-USV were highly consistent with laboratory analysis results, with determination coefficients (R2) of 0.71 and 0.77, respectively; and (4) meteorological parameters could be continuously and stably measured by WC-USV. Results demonstrated the feasibility and practicability of the WC-USV for automatic in situ observations. The USV provided a new way of thinking for the future development of intelligent automation of the aquatic remote sensing ground verification system. It could be a good option to conduct field investigations for ocean color remote sensing and provide an alternative for highly polluted and/or shallow high-risk waters which large vessels have difficulty reaching.

scholarly journals Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.7 Å inside cells

2020 ◽  
Author(s):  
Dimitry Tegunov ◽  
Liang Xue ◽  
Christian Dienemann ◽  
Patrick Cramer ◽  
Julia Mahamid
Keyword(s):  
Structure Determination ◽  
Software Tool ◽  
Series Data ◽  
Bacterial Cells ◽  
Sample Motion ◽  
70S Ribosome ◽  
Tilt Series ◽  
Particle Refinement

Cryo-electron microscopy (cryo-EM) enables macromolecular structure determination in vitro and in situ. In addition to aligning individual particles, accurate registration of sample motion and 3D deformation during exposures is crucial for achieving high resolution. Here we describe M, a software tool that establishes a reference-based, multi-particle refinement framework for cryo-EM data and improves the results of structure determination. M provides a unified optimization framework for both in vitro frame series and in situ tomographic tilt series data. We show that tilt series data can provide the same resolution as frame series, indicating that the alignment step no longer limits the resolution obtainable from tomographic data. In combination with Warp and RELION, M improves upon previous methods, and resolves a 70S ribosome bound to an antibiotic inside bacterial cells at a nominal resolution of 3.7 Å. Thus, computational tools are now available to resolve structures from tomographic in situ cryo-EM data at residue level.

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