scholarly journals The Concept of a Microwave Radar with an Asymmetric Knifelike Beam for the Remote Sensing of Ocean Waves

2005 ◽  
Vol 22 (11) ◽  
pp. 1809-1820 ◽  
Author(s):  
V. Yu Karaev ◽  
M. B. Kanevsky ◽  
G. N. Balandina ◽  
P. Challenor ◽  
C. Gommenginger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ocean Surface ◽  
Ocean Waves ◽  
Wave Processes ◽  
New Approach ◽  
Microwave Radar ◽  
Surface Slopes ◽  
Processing Procedure ◽  
New Processing ◽  
Range Selection

Abstract The features of near-nadir probing of the ocean surface from a satellite are investigated, and a new configuration of a microwave radar for the surface slopes measurement is proposed. Numerical modeling is used to examine the feasibility of a spaceborne implementation, to devise a measuring technique, and to develop a data processing algorithm. It is shown that the radar with a knifelike beam (1° × 25°–30°) can be used to measure the variance of surface wave slopes. Using range selection and a new processing procedure to synthesize data, it is possible to achieve the spatial resolution required to study wave processes on the ocean surface from satellites. A new approach to wind speed retrieval at and near nadir is also discussed.

Technical Note Is it possible to measure ocean surface slopes with a microwave radar at nadir probing?

2002 ◽  
Vol 23 (16) ◽  
pp. 3251-3262 ◽  
Author(s):  
V. Yu. Karaev ◽  
M. B. Kanevsky ◽  
P. D. Cotton ◽  
P. G. Challenor
Keyword(s):  
Ocean Surface ◽  
Technical Note ◽  
Microwave Radar ◽  
Surface Slopes

scholarly journals Fourier transforms for fast and quantitative Laser Speckle Imaging

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
J. Buijs ◽  
J. van der Gucht ◽  
J. Sprakel
Keyword(s):  
Quantitative Data ◽  
Fourier Transforms ◽  
Imaging Technique ◽  
Laser Speckle ◽  
Frequency Range ◽  
Laser Speckle Imaging ◽  
Speckle Imaging ◽  
New Approach ◽  
Speckle Patterns ◽  
New Processing

Abstract Laser speckle imaging is a powerful imaging technique that visualizes microscopic motion within turbid materials. At current two methods are widely used to analyze speckle data: one is fast but qualitative, the other quantitative but computationally expensive. We have developed a new processing algorithm based on the fast Fourier transform, which converts raw speckle patterns into maps of microscopic motion and is both fast and quantitative, providing a dynamnic spectrum of the material over a frequency range spanning several decades. In this article we show how to apply this algorithm and how to measure a diffusion coefficient with it. We show that this method is quantitative and several orders of magnitude faster than the existing quantitative method. Finally we harness the potential of this new approach by constructing a portable laser speckle imaging setup that performs quantitative data processing in real-time on a tablet.

SMART SENSOR: AN ON-BOARD IMAGE PROCESSING SYSTEM FOR REAL-TIME REMOTE SENSING

2002 ◽  
Vol 02 (03) ◽  
pp. 481-499
Author(s):  
JANE YOU ◽  
DAVID ZHANG
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Compression ◽  
Real Time ◽  
Processing System ◽  
Smart Sensor ◽  
Pipeline Architecture ◽  
New Approach ◽  
Hardware Processing ◽  
Sensor System Design

This paper presents a new approach to smart sensor system design for real-time remote sensing. A combination of techniques for image analysis and image compression is investigated. The proposed algorithms include: (1) a fractional discrimination function for image analysis, (2) a comparison of effective algorithms for image compression, (3) a pipeline architecture for parallel image classification and compression on-board satellites, and (4) a task control strategy for mapping image computing models to hardware processing elements. The efficiency and accuracy of the proposed techniques are demonstrated throughout system simulation.

scholarly journals Remote Sensing for Property Valuation: A Data Source Comparison in Support of Fair Land Taxation in Rwanda

2021 ◽  
Vol 13 (18) ◽  
pp. 3563
Author(s):  
Mila Koeva ◽  
Oscar Gasuku ◽  
Monica Lengoiboni ◽  
Kwabena Asiama ◽  
Rohan Mark Bennett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Primary Data ◽  
Structured Interviews ◽  
Remotely Sensed Data ◽  
Quality Of Data ◽  
New Approach ◽  
Property Valuation ◽  
Land Administration

Remotely sensed data is increasingly applied across many domains, including fit-for-purpose land administration (FFPLA), where the focus is on fast, affordable, and accurate property information collection. Property valuation, as one of the main functions of land administration systems, is influenced by locational, physical, legal, and economic factors. Despite the importance of property valuation to economic development, there are often no standardized rules or strict data requirements for property valuation for taxation in developing contexts, such as Rwanda. This study aims at assessing different remote sensing data in support of developing a new approach for property valuation for taxation in Rwanda; one that aligns with the FFPLA philosophy. Three different remote sensing technologies, (i) aerial images acquired with a digital camera, (ii) WorldView2 satellite images, and (iii) unmanned aerial vehicle (UAV) images obtained with a DJI Phantom 2 Vision Plus quadcopter, are compared and analyzed in terms of their fitness to fulfil the requirements for valuation for taxation purposes. Quantitative and qualitative methods are applied for the comparative analysis. Prior to the field visit, the fundamental concepts of property valuation for taxation and remote sensing were reviewed. In the field, reference data using high precision GNSS (Leica) was collected and used for quantitative assessment. Primary data was further collected via semi-structured interviews and focus group discussions. The results show that UAVs have the highest potential for collecting data to support property valuation for taxation. The main reasons are the prime need for accurate-enough and up-to-date information. The comparison of the different remote sensing techniques and the provided new approach can support land valuers and professionals in the field in bottom-up activities following the FFPLA principles and maintaining the temporal quality of data needed for fair taxation.

scholarly journals HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS

2019 ◽  
Vol 1 (1) ◽  
pp. 25-37
Author(s):  
Mohamad M. Awad
Keyword(s):  
Remote Sensing ◽  
Eastern Mediterranean ◽  
Hyperspectral Data ◽  
Spectral Angle Mapper ◽  
New Approach ◽  
Spectral Signatures ◽  
Agriculture Sector ◽  
Supervised Segmentation ◽  
Eastern Mediterranean Basin ◽  
Crop Mapping

In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping.  The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).

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