scholarly journals A Real Time Mosaic Method for Remote Sensing Video Images from UAV

2013 ◽  
Vol 04 (03) ◽  
pp. 168-172 ◽  
Author(s):  
Yang Yang ◽  
Guangmin Sun ◽  
Dequn Zhao ◽  
Bo Peng
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Video Images

scholarly journals Real-Time Semantic Segmentation of Remote Sensing Images Based on Bilateral Attention Refined Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28349-28360
Author(s):  
Jiali Cai ◽  
Chunjuan Liu ◽  
Haowen Yan ◽  
Xiaosuo Wu ◽  
Wanzhen Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Semantic Segmentation ◽  
Remote Sensing Images

Design of FPGA-Based Missile-Borne Image Acquisition and Storage Recorder

2015 ◽  
Vol 713-715 ◽  
pp. 1448-1451
Author(s):  
Lin Lu ◽  
Yan Feng Zhang ◽  
Xiao Feng Li
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Storage System ◽  
Large Data ◽  
Engineering Practice ◽  
Image Storage ◽  
Video Images ◽  
Stable Performance ◽  
And Storage ◽  
Time Image

The high-altitude missile and other special application occasions have requirements on image storage system, such as small size, high storage speed, low temperature resistance, etc. Commonly used image storage system in the market cannot meet such requirement. In the paper, real-time image storage system solutions on missile based on FPGA should be proposed. The system mainly consists of acquisition module and memory reading module. The whole system adopts FPGA as main control chip for mainly completing real-time decoding and acquisition on one path of PAL format video images, reading and writing of NandFlash chipset, erasure, bad block management and so on. The solution has passed various environmental tests with stable performance, large data storage capacity and easy expansion, which has been used in engineering practice.

Resource management of GEO relays for real-time remote sensing

2021 ◽  
Author(s):  
Xiaobin Xu ◽  
Hui Zhao ◽  
Chang Liu ◽  
Qi Wang ◽  
Shangguang Wang
Keyword(s):  
Remote Sensing ◽  
Resource Management ◽  
Real Time

Real time monitoring fire detection Using Remote Sensing

2021 ◽  
Author(s):  
Muhammad Iqbal Habibie ◽  
Nety Nurda ◽  
Harun Idham Akbar ◽  
Oni Bibin Bintoro ◽  
Robby Arifandri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Fire Detection ◽  
Real Time Monitoring

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

Remote Sensing in Oil Spill Handling in Offshore North West Java

2021 ◽  
Author(s):  
Audra Ligafinza ◽  
Farasdaq Muchibbus Sajjad ◽  
Mohammad Abdul Jabbar ◽  
Anggia Fatmawati ◽  
Alvin Derry Wirawan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Oil Spill ◽  
Environmental Damage ◽  
Satellite Imaging ◽  
North West ◽  
Novel Technologies ◽  
Oil Boom ◽  
Remediation Strategies ◽  
Restoration Strategies

Abstract During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.

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.

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