scholarly journals Payload Configurations for Efficient Image Acquisition – Indian Perspective

2014 ◽  
Vol XL-8 ◽  
pp. 1201-1206 ◽  
Author(s):  
D. R. M. Samudraiah ◽  
M. Saxena ◽  
S. Paul ◽  
P. Narayanababu ◽  
S. Kuriakose ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
High Efficiency ◽  
Image Acquisition ◽  
Image Data ◽  
Space Research ◽  
Remotely Sensed Data ◽  
Data Utilization ◽  
Spectral Bands ◽  
The World

The world is increasingly depending on remotely sensed data. The data is regularly used for monitoring the earth resources and also for solving problems of the world like disasters, climate degradation, etc. Remotely sensed data has changed our perspective of understanding of other planets. With innovative approaches in data utilization, the demands of remote sensing data are ever increasing. More and more research and developments are taken up for data utilization. The satellite resources are scarce and each launch costs heavily. Each launch is also associated with large effort for developing the hardware prior to launch. It is also associated with large number of software elements and mathematical algorithms post-launch. The proliferation of low-earth and geostationary satellites has led to increased scarcity in the available orbital slots for the newer satellites. Indian Space Research Organization has always tried to maximize the utility of satellites. Multiple sensors are flown on each satellite. In each of the satellites, sensors are designed to cater to various spectral bands/frequencies, spatial and temporal resolutions. Bhaskara-1, the first experimental satellite started with 2 bands in electro-optical spectrum and 3 bands in microwave spectrum. The recent Resourcesat-2 incorporates very efficient image acquisition approach with multi-resolution (3 types of spatial resolution) multi-band (4 spectral bands) electro-optical sensors (LISS-4, LISS-3* and AWiFS). The system has been designed to provide data globally with various data reception stations and onboard data storage capabilities. Oceansat-2 satellite has unique sensor combination with 8 band electro-optical high sensitive ocean colour monitor (catering to ocean and land) along with Ku band scatterometer to acquire information on ocean winds. INSAT- 3D launched recently provides high resolution 6 band image data in visible, short-wave, mid-wave and long-wave infrared spectrum. It also has 19 band sounder for providing vertical profile of water vapour, temperature, etc. The same system has data relay transponders for acquiring data from weather stations. The payload configurations have gone through significant changes over the years to increase data rate per kilogram of payload. Future Indian remote sensing systems are planned with very high efficient ways of image acquisition. <br><br> This paper analyses the strides taken by ISRO (Indian Space research Organisation) in achieving high efficiency in remote sensing image data acquisition. Parameters related to efficiency of image data acquisition are defined and a methodology is worked out to compute the same. Some of the Indian payloads are analysed with respect to some of the system/ subsystem parameters that decide the configuration of payload. Based on the analysis, possible configuration approaches that can provide high efficiency are identified. A case study is carried out with improved configuration and the results of efficiency improvements are reported. This methodology may be used for assessing other electro-optical payloads or missions and can be extended to other types of payloads and missions.

A Lightweight SE-YOLOv3 Network for Multi-Scale Object Detection in Remote Sensing Imagery

2021 ◽  
Author(s):  
Lifang Zhou ◽  
Guang Deng ◽  
Weisheng Li ◽  
Jianxun Mi ◽  
Bangjun Lei
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Computational Cost ◽  
Image Data ◽  
Detection Accuracy ◽  
Data Sets ◽  
One Stage ◽  
Efficiency And Effectiveness ◽  
Spatial Pyramid Pooling ◽  
Feature Augmentation

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.

Geostatistics and remote sensing

1998 ◽  
Vol 22 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Paul J. Curran ◽  
Peter M. Atkinson
Keyword(s):  
Remote Sensing ◽  
Spatial Variation ◽  
Conditional Simulation ◽  
Image Data ◽  
Remotely Sensed ◽  
Continuous Variables ◽  
Remotely Sensed Data ◽  
Sampling Schemes ◽  
Optimum Sampling ◽  
Design Optimum

In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.

scholarly journals DESIGN AND VERIFICATION OF REMOTE SENSING IMAGE DATA CENTER STORAGE ARCHITECTURE BASED ON HADOOP

2018 ◽  
Vol XLII-3 ◽  
pp. 1639-1642
Author(s):  
D. Tang ◽  
X. Zhou ◽  
Y. Jing ◽  
W. Cong ◽  
C. Li
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Data Center ◽  
High Speed ◽  
Large Scale ◽  
Distributed Storage ◽  
Image Data ◽  
Remotely Sensed Data ◽  
Processing Technologies ◽  
Data Parallel

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

scholarly journals UPSCALING AND VALIDATION OF RTK-DIRECT GEOREFERENCED UAV-BASED RGB IMAGE DATA WITH PLANET IMAGERY USING POLYGON GRIDS FOR PASTURE MONITORING

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 533-538
Author(s):  
G. Bareth ◽  
C. Hütt
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Image Data ◽  
Spatial Decision Support ◽  
Analysis Techniques ◽  
Remote Sensing Methods ◽  
Very High ◽  
Rgb Image

Abstract. The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techniques or in the focus of crop and grassland monitoring and provide very high spatial resolutions. In contrast, satellite data are only available in high to moderate spatial resolutions. In this contribution, we introduce direct georeferenced data acquisition with a Phantom 4 RTK for pasture monitoring and investigate the upscaling of the cm data to satellite resolutions using polygon grids.

The Application of 3D Laser Scanning Technology in Ancient Architecture Protection

2013 ◽  
Vol 353-356 ◽  
pp. 3476-3479
Author(s):  
Jun Lan Zhao ◽  
Ran Wu ◽  
Lei Wang ◽  
Yi Qin Wu
Keyword(s):  
3D Reconstruction ◽  
Data Acquisition ◽  
High Speed ◽  
Laser Scanning ◽  
Large Scale ◽  
High Efficiency ◽  
Image Data ◽  
High Accuracy ◽  
3D Laser Scanning ◽  
3D Data

The study of 3D laser scanning technology in Category Conservation is one of the hot researches in recent years. Through the high-speed laser scanning, catching the 3D data of an object in large-scale with high efficiency, high accuracy and excellent resolution, is a new way in 3D reconstruction and image data acquisition. The method has achieved good results through the experiment.

scholarly journals A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena

2018 ◽  
Vol 7 (4.15) ◽  
pp. 447 ◽  
Author(s):  
W Wanayumini ◽  
O S Sitompul ◽  
M Zarlis ◽  
Saib Suwilo ◽  
A M H Pardede
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Data Acquisition ◽  
Image Classification ◽  
Discrete Cosine Transform ◽  
Supervised Classification ◽  
Image Data ◽  
New Model ◽  
Best Value ◽  
Supervised Image Classification

Unattended classification is a classification which is the process of forming classes conducted by computers. The classes formed in this classification are highly dependent on data acquisition. In the process, this classification classifies pixels based on similarity or spectral similarity. While the supervised classification is a classification carried out by the analyst's direction. The purpose of this study is to build a new model of image-based classification based on chaos phenomena through remote sensing to detect the beginning of the emergence of tornadoes. This research optimizes the search for the best value from a data collection of samples of chaos phenomena in tornadoes through a new model called Citra which is supervised by Chaos Discrete Cosine Transform Spectral Angel Mapper Classification (SiChDCosTSamC). The resulting model can then be used as remote sensing to detect the appearance of the initial tornado. Tests will be carried out using the Protected Image Welding on models based on chaotic / chaotic phenomena. Testing will be carried out on a collection of sample image data sourced from SIO, NOAA, US data. Navy, NGA, GEBCO U.S. PGA / NASA Google IBCAO Geological Geological Survey / Copernicus.  

Measuring the response of pasture to superphosphate using aircraft and satellite remote sensing

1993 ◽  
Vol 33 (5) ◽  
pp. 597 ◽  
Author(s):  
RND Reid ◽  
PJ Vickery ◽  
DA Hedges ◽  
PM Williams
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Near Infrared ◽  
Processing System ◽  
Remote Sensing Data ◽  
Image Data ◽  
Short Wave ◽  
Stocking Rate ◽  
Remotely Sensed Data ◽  
Resultant Data

Remote sensing measurements in the visible, near infrared, and short-wave infrared were made on experimental areas of grass-legume pasture with different fertiliser and stocking rate treatments and on commercial pastures with added fertiliser.Divisive classification and ordination analyses of the remotely sensed data were used to allocate the image data to 11-15 classes from measurements in 2 successive years The resultant data were displayed on an image processing system which showed that the fertilised areas belonged to classes different from those without fertiliser. Soil and plant nutrient tests revealed differences between treated and untreated sites as mapped from the remote sensing data.

scholarly journals A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena

2019 ◽  
Author(s):  
Akim Manaor Hara Pardede
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Data Acquisition ◽  
Image Classification ◽  
Discrete Cosine Transform ◽  
Supervised Classification ◽  
Image Data ◽  
New Model ◽  
Best Value ◽  
Supervised Image Classification

Unattended classification is a classification which is the process of forming classes conducted by computers. The classes formed in this classification are highly dependent on data acquisition. In the process, this classification classifies pixels based on similarity or spectral similarity. While the supervised classification is a classification carried out by the analyst's direction. The l purpose of this study is to build a new model of image-based classification based on chaos phenomena through remote sensing to detect the beginning of the emergence of tornadoes. This research optimizes the search for the best value from a data collection of samples of chaos phenomena in tornadoes through a new model called Citra which is supervised by Chaos Discrete Cosine Transform Spectral Angel Mapper Classification (SiChDCosTSamC). The resulting model can then be used as remote sensing to detect the appearance of the initial tornado. Tests will be carried out using the Protected Image Welding on models based on chaotic / chaotic phenomena. Testing will be carried out on a collection of sample image data sourced from SIO, NOAA, US data. Navy, NGA, GEBCO U.S. PGA / NASA Google IBCAO Geological Geological Survey / Copernicus

scholarly journals Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

2019 ◽  
Vol 9 (13) ◽  
pp. 2737 ◽  
Author(s):  
Avihai Ron ◽  
Neda Davoudi ◽  
Xosé Luís Deán-Ben ◽  
Daniel Razansky
Keyword(s):  
Data Acquisition ◽  
Respiratory Motion ◽  
Image Acquisition ◽  
Three Dimensional ◽  
Image Data ◽  
Motion Artifacts ◽  
Whole Body ◽  
Cross Sectional ◽  
Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.

Designation of Image Acquisition Module Based on the C8051F340

2012 ◽  
Vol 588-589 ◽  
pp. 769-772
Author(s):  
Xin Bin Zhang ◽  
Shi Zhong Li
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Simple Structure ◽  
Image Acquisition ◽  
Image Data ◽  
Image Sensor ◽  
Software Implementation ◽  
Wireless Transmission ◽  
Data Acquisition Module

The image data acquisition module mainly completes the collection of image data and transmits the collected data to the wireless transmission module. This paper mainly discusses the hardware components of the image acquisition module and software implementation. The collected image quality influences image processing, therefore the choice of image sensor is an important part of this system. This is a OV7670 image sensor. The master controller uses C8051F340 MCU. C8051F MC’s frequency has been greatly improved compared with ordinary 51 MCU, and has the advantages of simple structure, interface expansion capability, low prices and better performance.

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