A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing

2010 ◽  
Vol 114 (11) ◽  
pp. 2637-2650 ◽  
Author(s):  
Grégory Duveiller ◽  
Pierre Defourny
Keyword(s):  
Remote Sensing ◽  
Conceptual Framework ◽  
Spatial Resolution ◽  
Resolution Requirements

scholarly journals Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing

2014 ◽  
Vol 6 (9) ◽  
pp. 9034-9063 ◽  
Author(s):  
Fabian Löw ◽  
Grégory Duveiller
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Optical Remote Sensing ◽  
Resolution Requirements

scholarly journals Onboard equipment of L- and X-bands synthesized aperture radars for remote sensing of the Earth with higher resolution: requirements and implementation

2019 ◽  
Vol 30 ◽  
pp. 15032
Author(s):  
Mikhail Rovkin
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Flight Tests ◽  
Bench Tests ◽  
The Earth ◽  
X Band ◽  
Main Components ◽  
Onboard Equipment ◽  
Resolution Requirements

Requirements to the equipment of dual (L - and X-) band SAR, realizing increase of spatial resolution due to expansion of a spectrum of sounding signals are set. The technical decision of the main components of the onboard equipment and the results of bench tests of the layout of small - sized SAR of L - and X-bands for remote sensing of the Earth prepared for flight tests are described.

scholarly journals Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

2019 ◽  
Vol 45 (3) ◽  
pp. 434-443 ◽  
Author(s):  
John Lekki ◽  
Eric Deutsch ◽  
Mike Sayers ◽  
Karl Bosse ◽  
Robert Anderson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Lakes ◽  
Spatial Resolution ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Resolution Requirements

The Conceptual Framework Design of Intelligent Multi-feature Remote Sensing Image Retrieval

2011 ◽  
Vol 13 (3) ◽  
pp. 401-408
Author(s):  
Qin DAI ◽  
Jianbo LIU ◽  
Shibin LIU
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Conceptual Framework ◽  
Remote Sensing Image ◽  
Framework Design

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

Multiscale U-Shaped CNN Building Instance Extraction Framework With Edge Constraint for High-Spatial-Resolution Remote Sensing Imagery

2020 ◽  
pp. 1-15
Author(s):  
Yuanyuan Liu ◽  
Dingyuan Chen ◽  
Ailong Ma ◽  
Yanfei Zhong ◽  
Fang Fang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Imagery

Extracting Man-Made Objects From High Spatial Resolution Remote Sensing Images via Fast Level Set Evolutions

2015 ◽  
Vol 53 (2) ◽  
pp. 883-899 ◽  
Author(s):  
Zhongbin Li ◽  
Wenzhong Shi ◽  
Qunming Wang ◽  
Zelang Miao
Keyword(s):  
Remote Sensing ◽  
Level Set ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images
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