The processing and use of data from Earth observation satellites

1984 ◽  
Vol 312 (1519) ◽  
pp. 109-114
Keyword(s):  
Remote Sensing ◽  
Earth Observation ◽  
Advanced Technology ◽  
System Designer ◽  
Satellite Systems ◽  
Use Of Data ◽  
Observation Systems ◽  
Earth Observation Satellite ◽  
Institutional Aspects ◽  
Earth Observation Satellites

Most systems reliant on advanced technology present a familiar dilemma: the system designer does not know what the customer wants, while the customer does not understand the technology well enough to know what is possible. Although Earth observation satellite systems ought ideally to be designed for all customer needs, this is impossible for several reasons. Not least of these is the difficulty of identifying at the outset all, or even most, of the possible customers. This circumstance makes the creation of Earth observation systems somewhat speculative and imposes particular constraints on the subsystems for processing and use of the data. This paper discusses the technical and institutional aspects of processing and dissemination of data from remote-sensing satellites for the benefit of the user.

scholarly journals A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6660
Author(s):  
Lihao Liu ◽  
Zhenghong Dong ◽  
Haoxiang Su ◽  
Dingzhan Yu
Keyword(s):  
Dynamic Scheduling ◽  
Environmental Changes ◽  
Search Algorithm ◽  
Integrated Management ◽  
Scheduling Algorithm ◽  
Earth Observation ◽  
Scheduling Problems ◽  
Satellite Systems ◽  
Earth Observation Satellites ◽  
Negotiation Mechanism

While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

Understanding satellite remote sensing

2019 ◽  
pp. 1-19
Author(s):  
Nathalie Pettorelli
Keyword(s):  
Remote Sensing ◽  
Environmental Management ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Earth Observation ◽  
Complex Nature ◽  
Active Sensors ◽  
Earth Observations ◽  
Earth Observation Satellites

This chapter seeks to provide a quick introduction to satellite remote sensing. It starts with a set of definitions, thereby to explain the differences between Earth observations, remote sensing, and satellite remote sensing. It then goes on to describe how satellite remote sensing works, and what the differences between passive and active sensors are. An introduction to the main sensors currently on board active civilian Earth observation satellites is provided, together with details on their key specifications. The complex nature of satellite data, as well as the tools required to manipulate and analyse them are discussed. The chapter ends with a presentation of the main issues to be aware of when dealing with satellite data, and a look at the coming sensors and datasets that will soon expand opportunities for satellite data to inform environmental management.

scholarly journals DATA QUALITY IN REMOTE SENSING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 447-453 ◽  
Author(s):  
C. Batini ◽  
T. Blaschke ◽  
S. Lang ◽  
F. Albrecht ◽  
H. M. Abdulmutalib ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Earth Observation ◽  
Data Sets ◽  
New Era ◽  
Group Iii ◽  
Future Challenges ◽  
Earth Observation Satellites

The issue of data quality (DQ) is of growing importance in Remote Sensing (RS), due to the widespread use of digital services (incl. apps) that exploit remote sensing data. In this position paper a body of experts from the ISPRS Intercommission working group III/IVb “DQ” identifies, categorises and reasons about issues that are considered as crucial for a RS research and application agenda. This ISPRS initiative ensures to build on earlier work by other organisations such as IEEE, CEOS or GEO, in particular on the meritorious work of the Quality Assurance Framework for Earth Observation (QA4EO) which was established and endorsed by the Committee on Earth Observation Satellites (CEOS) but aims to broaden the view by including experts from computer science and particularly database science. The main activities and outcomes include: providing a taxonomy of DQ dimensions in the RS domain, achieving a global approach to DQ for heterogeneous-format RS data sets, investigate DQ dimensions in use, conceive a methodology for managing cost effective solutions on DQ in RS initiatives, and to address future challenges on RS DQ dimensions arising in the new era of the big Earth data.

scholarly journals An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling

2020 ◽  
Vol 10 (16) ◽  
pp. 5616 ◽  
Author(s):  
Yuning Chen ◽  
Ji Lu ◽  
Renjie He ◽  
Junwei Ou
Keyword(s):  
Local Search ◽  
Programming Model ◽  
Image Data ◽  
Earth Observation ◽  
Search Heuristic ◽  
Algorithm Efficiency ◽  
Integrated Scheduling ◽  
Local Search Heuristic ◽  
Earth Observation Satellite ◽  
Earth Observation Satellites

Earth observation satellites (EOSs) are taking a large number of pictures with increasing resolution which produce massive image data. Satellite data transmission becomes the bottleneck part in the process of EOS resource management. In this paper, we study the earth observation satellite integrated scheduling problem (EOSIS) where the imaging activities and download activities are considered integratively. We propose an integer linear programming model to formulate the problem. Due to the NP-hardness of the problem, we propose an efficient local search heuristic (ELSH) to solve problems of large size. ELSH uses a dedicated local search method to guarantee algorithm performance and efficient constraint handling mechanisms to guarantee algorithm efficiency. Numerical experimental results show that the algorithm demonstrates excellent performance on a set of benchmark instances. The ELSH achieves optimal results for all small-size instances (with 50 targets, two satellites, and three ground stations), and is very robust for large instances with up to 2000 targets. Moreover, the proposed ELSH easily dominates the state-of-the-art algorithm.

An Intelligent Approach to Dynamic Scheduling System of Earth Observation Satellites

2011 ◽  
Vol 186 ◽  
pp. 591-595
Author(s):  
Yao Feng ◽  
Ren Jie He ◽  
Ju Fang Li ◽  
Li Ning Xing
Keyword(s):  
Dynamic Scheduling ◽  
Satellite System ◽  
Earth Observation ◽  
Benchmark Problems ◽  
High Quality ◽  
Scheduling System ◽  
Earth Observation Satellite ◽  
Earth Observation Satellites ◽  
Intelligent Approach ◽  
Hierarchical Genetic Algorithm

With the increased number of earth observation satellites, the process of acquiring high quality solution schedule for multi-satellite, multi-orbit and multi-user is more difficult than before. The multi-objective hierarchical genetic algorithm with preference and dynamic heuristic algorithm are proposed to solve the dynamic scheduling problem of earth observation satellite system. The experimental results performed on some benchmark problems suggest that this proposed approach is effective to the dynamic scheduling system.

scholarly journals THE 2015 CEOS DISTANCE EDUCATION COURSE ON GEOLOGICAL DISASTERS MANAGEMENT

2017 ◽  
Vol 50 (3) ◽  
pp. 1643
Author(s):  
A. Mouratidis
Keyword(s):  
Remote Sensing ◽  
Distance Education ◽  
Disaster Management ◽  
Working Group ◽  
Earth Observation ◽  
Joint Effort ◽  
Geological Disasters ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Earth Observation Satellites

The purpose of this paper is to present the framework, content, outcomes and the lessons learnt from the 2015 Committee on Earth Observation Satellites (CEOS) course on geological disasters management, delivered within the 2015 CEOS Distance Education Course entitled “Remote Sensing Technology for Disaster Management” - a joint effort by CEOS Agencies, in particular of the Working Group on Capacity Building & Data Democracy (WGCapD) and the Working Group on Disasters (WGDisasters).

scholarly journals CHALLENGES IN OPERATIONS AND MAINTENANCE OF ANTARCTICA GROUND STATION FOR EARTH OBSERVATION SATELLITES

2018 ◽  
Vol XLII-5 ◽  
pp. 281-283
Author(s):  
N. Mangala Mani ◽  
S. Ghetiya ◽  
B. Srikanth ◽  
C. S. Padmavthi ◽  
G. Uma Devi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Geographical Location ◽  
Austral Summer ◽  
Earth Observation ◽  
Geographical Information ◽  
Satellite Orbits ◽  
Polar Regions ◽  
Local Conditions ◽  
Ground Station ◽  
Earth Observation Satellites

<p><strong>Abstract.</strong> Remote sensing and Geographical Information System (GIS) through Earth Observation (EO) Satellites have become most important tools for many of the societal applications of day-to-day life, during the past few decades of the 21st century, and this need is rapidly increasing. Presently, EO payload (PLD) data from Polar Orbiting Indian Remote Sensing (IRS) Satellite Missions is received at the Integrated Multi-mission Ground segment for Earth Observation Satellites (IMGEOS) at NRSC, Hyderabad, India. But at this geographical location, only four out of fourteen orbits data can be acquired from each of the polar satellites. These polar satellite orbits converge at poles, hence establishing a ground station at Polar Regions has an advantage of acquiring data from ten to twelve orbits.</p><p>To increase the capability, a data reception Ground Station and Communication facility was established by ISRO at Bharati Station, Antarctica, located at 69° South and 76° East, in 2012–2013 (Austral Summer). This station is maintained for flawless operations and smooth flow of IRS Satellites EO data from Antarctica, by three Scientists/Engineers from ISRO, who overwinter at Antarctica, along with other Expedition members, by clearing the pre-requisites for an Antarctica Expedition. While this location is suitable to establish a Ground Station, the local conditions are a challenge to be faced (when established then, and now) to operate and maintain it 24<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>7 throughout the year.</p>

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