Evaluation of Structures and Methods for Resolution Determination of Remote Sensing Sensors

2020 ◽  
pp. 59-69
Author(s):  
Henry Meißner ◽  
Michael Cramer ◽  
Ralf Reulke
Keyword(s):  
Remote Sensing

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume

Automatic image referencing in satellite surveying

2021 ◽  
pp. 458-463
Author(s):  
V.A. Pantyushin ◽  
A.A. Andreev
Keyword(s):  
Remote Sensing ◽  
Topographic Maps ◽  
Automatic Determination ◽  
Digital Maps ◽  
The Earth ◽  
Areal Coverage ◽  
Fully Automatic

The method of coordinate referencing of remote sensing materials in the process of satellite surveying is proposed in this paper. The method provides automatic determination of position of the survey routes and images in the routes on the surface of the Earth ellipsoid with their subsequent displaying on the nomenclature sheets of topographic maps on the composite table. The method provides for fully automatic development of a scheme of areal coverage with survey materials in the presence of data on the coordinates of photographing points obtained at the moments of exposure. The algorithm can be used to link images to the corresponding areas of electronic and digital maps.

scholarly journals Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Field Survey ◽  
Critical Level ◽  
Satellite Image ◽  
Geographic Information ◽  
Level Data ◽  
The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

Quantitative Determination of Soil Erosion and Prioritization of Micro-Watersheds Using Remote Sensing and GIS

2011 ◽  
Vol 39 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Vipul Shinde ◽  
Arabinda Sharma ◽  
Kamlesh N. Tiwari ◽  
Manjushree Singh
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Quantitative Determination ◽  
Remote Sensing And Gis

scholarly journals Geometric Accuracy Improvement Method for High-Resolution Optical Satellite Remote Sensing Imagery Combining Multi-Temporal SAR Imagery and GLAS Data

2020 ◽  
Vol 12 (3) ◽  
pp. 568
Author(s):  
Quansheng Zhu ◽  
Wanshou Jiang ◽  
Ying Zhu ◽  
Linze Li
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Geometric Accuracy ◽  
Laser Altimetry ◽  
Multi Temporal ◽  
Improvement Method ◽  
Optical Imagery ◽  
Sar Imagery

With the widespread availability of satellite data, a single region can be described using multi-source and multi-temporal remote sensing data, such as high-resolution (HR) optical imagery, synthetic aperture radar (SAR) imagery, and space-borne laser altimetry data. These have become the main source of data for geopositioning. However, due to the limitation of the direct geometric accuracy of HR optical imagery and the effect of the small intersection angle of HR optical imagery in stereo pair orientation, the geometric accuracy of HR optical imagery cannot meet the requirements for geopositioning without ground control points (GCPs), especially in uninhabited areas, such as forests, plateaus, or deserts. Without satellite attitude error, SAR usually provides higher geometric accuracy than optical satellites. Space-borne laser altimetry technology can collect global laser footprints with high altitude accuracy. Therefore, this paper presents a geometric accuracy improvement method for HR optical satellite remote sensing imagery combining multi-temporal SAR Imagery and GLAS data without GCPs. Based on the imaging mechanism, the differences in the weight matrix determination of the HR optical imagery and SAR imagery were analyzed. The laser altimetry data with high altitude accuracy were selected and applied as height control point in combined geopositioning. To validate the combined geopositioning approach, GaoFen2 (GF2) optical imagery, GaoFen6 (GF6) optical imagery, GaoFen3 (GF3) SAR imagery, and the Geoscience Laser Altimeter System (GLAS) footprint were tested. The experimental results show that the proposed model can be effectively applied to combined geopositioning to improve the geometric accuracy of HR optical imagery. Moreover, we found that the distribution and weight matrix determination of SAR images and the distribution of GLAS footprints are the crucial factors influencing geometric accuracy. Combined geopositioning using multi-source remote sensing data can achieve a plane accuracy of 1.587 m and an altitude accuracy of 1.985 m, which is similar to the geometric accuracy of geopositioning of GF2 with GCPs.

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