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.  

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 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.

The Comparative Study of Remote Sensing Image Classification Method Based on ERDAS

2012 ◽  
Vol 546-547 ◽  
pp. 542-547 ◽  
Author(s):  
Guang Wei Zeng ◽  
Gui Fen Chen ◽  
Chu Nan Li ◽  
Jiao Ye
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Arable Land ◽  
Processing System ◽  
Image Data ◽  
Unsupervised Classification ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Remote Sensing Image Classification ◽  
Erdas Imagine

ERDAS IMAGINE is a remote sensing image processing system developed by the United States.The paper using ERDAS to classified the remote sensing of Land-sat TM image data by supervised classification method and unsupervised classification method, Using the Yushu City remote sensing image of Jilin Province as the trial data, and classified the forest, arable land and water from the remote sensing images, compared the test data of the supervised classification and unsupervised classification method, shows that the supervised classification method can be better to solute the questions "with the spectrum of foreign body" and "synonyms spectrum" than unsupervised classification method, and optimize classification images, improved information extraction accuracy. The application shows the classification result is consistent with the actual situation and it laid the foundation for land to have the rational planning and use.

scholarly journals A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1211
Author(s):  
Adeel Ahmad ◽  
Sajid Rashid Ahmad ◽  
Hammad Gilani ◽  
Aqil Tariq ◽  
Na Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Constitutional Amendment ◽  
Forest Mapping ◽  
Remote Sensing Classification ◽  
Sensing Data ◽  
Forest Assessment ◽  
Before And After ◽  
Supervised Image Classification

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

scholarly journals Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

2015 ◽  
Vol XL-7/W4 ◽  
pp. 75-80 ◽  
Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Image Data ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Level ◽  
Image Object

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.

scholarly journals PURIFICATION OF TRAINING SAMPLES BASED ON SPECTRAL FEATURE AND SUPERPIXEL SEGMENTATION

2018 ◽  
Vol XLII-3 ◽  
pp. 425-430
Author(s):  
X. Guan ◽  
W. Qi ◽  
J. He ◽  
Q. Wen ◽  
T. Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Spatial Resolution ◽  
Spectral Feature ◽  
Ground Truth ◽  
Training Data ◽  
Remote Sensing Images ◽  
Coverage Area ◽  
Business Programs ◽  
Supervised Image Classification

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

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.

scholarly journals Estimation of debris cover and its temporal variation using optical satellite sensor data: a case study in Chenab basin, Himalaya

2009 ◽  
Vol 55 (191) ◽  
pp. 444-452 ◽  
Author(s):  
A. Shukla ◽  
R.P. Gupta ◽  
M.K. Arora
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Image Data ◽  
Optical Remote Sensing ◽  
Glacier Area ◽  
Sensing Data ◽  
Debris Cover ◽  
Topographical Maps

AbstractDebris cover over glaciers greatly affects their rate of ablation and is a sensitive indicator of glacier health. This study focuses on estimation of debris cover over Samudratapu glacier, Chenab basin, Himalaya, using optical remote-sensing data. Remote-sensing image data of IRS-1C LISS-III (September 2001), IRS-P6 AWiFS (September 2004) and Terra ASTER (September 2004) along with Survey of India topographical maps (1963) were used in the study. Supervised classification of topographically corrected reflectance image data was systematically conducted to map six land-cover classes in the glacier terrain: snow, ice, mixed ice and debris, debris, valley rock, and water. An accuracy assessment of the classification was conducted using the ASTER visible/near-infrared data as the reference. The overall accuracies of the glacier-cover maps were found to range from 83.7% to 89.1%, whereas the individual class accuracy of debris-cover mapping was found to range from 82% to 95%. This shows that supervised classification of topographically corrected reflectance data is effective for the extraction of debris cover. In addition, a comparative study of glacier-cover maps generated from remote-sensing data (supervised classification) of September 2001 and September 2004 and Survey of India topographical maps (1963) has highlighted the trends of glacier depletion and recession. The glacier snout receded by about 756 m from 1963 to 2004, and the total glacier area was reduced by 13.7 km2 (from 110 km2 in 1963). Further, glacier retreat is found to be accompanied by a decrease in mixed ice and debris and a marked increase in debris-cover area. The area covered by valley rock is found to increase, confirming an overall decrease in the glacier area. The results from this study demonstrate the applicability of optical remote-sensing data in monitoring glacier terrain, and particularly mapping debris-cover area.

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