High performance SIFT features clustering of VHR satellite images for disaster management

2015 ◽  
Author(s):  
Ujwala Bhangale ◽  
Surya Durbha
Keyword(s):  
Disaster Management ◽  
High Performance ◽  
Satellite Images ◽  
Sift Features

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

scholarly journals AN ANALYSIS OF REMOTE SENSING SENSOR ISSUES AND SENSOR CROSS CALIBRATION

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sheron Henry Christy
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Disaster Management ◽  
Satellite Images ◽  
Good Alternative ◽  
Point Of View ◽  
Sensor Calibration ◽  
Alternative Technology ◽  
Irs P6 ◽  
Cross Calibration

Remote sensing is a very good alternative technology for managing natural resources as compared to conventional technologies. This paper highlights the various challenges in UAS sensors. Comparison of IRS-P6 and Land Sat sensors is described from accuracy point of view by covering same areas by both the sensors which gives the performance features of both the sensors. Inter sensor calibration is depicted to realize its importance in applications like precision farming, disaster management, etc. requiring multiple dated satellite images.

scholarly journals Design and Development of High-Performance Intelligent Disaster Management System in IoT Paradigm – A Novel Architecture and Review on Challenges in real time Hazards Monitoring

2021 ◽  
Vol 10 (3) ◽  
pp. 16-21
Author(s):  
Sachin Chavhan ◽  
Rahul Chavhan
Keyword(s):  
Disaster Management ◽  
Social Life ◽  
Disaster Response ◽  
Management System ◽  
High Performance ◽  
Design And Development ◽  
Satellite Mission ◽  
Data Production ◽  
Novel Design ◽  
Disaster Management System

In recent years India is suffering from various natural disaster which have great effect on social life of people and large burden on disaster management authority. The high frequency disaster causes a serious loss of property, life and present more complex damage. Disaster management involves the detailed process of disaster response. A High-Performance Intelligent Disaster Management System can realize the complete disaster avoidance and reduction from satellite mission planning, data production, data acquisition, with the application of remote sensing and managing integrated rapid service. The main objective of proposed work is to overcome the limitations of disaster management with a novel design and development of IoT based platform for the application of disaster management system.

scholarly journals International Charter Support during Major Flood Disasters in India

2014 ◽  
Vol XL-8 ◽  
pp. 1501-1505
Author(s):  
G. Srinivasa Rao ◽  
C. M. Bhatt ◽  
P. G. Diwaker
Keyword(s):  
Disaster Management ◽  
Satellite Data ◽  
Communication Systems ◽  
Satellite Images ◽  
Temporal Frequency ◽  
Weather Conditions ◽  
Satellite Observation ◽  
Jammu And Kashmir ◽  
Major Disaster ◽  
Multi Temporal

Earth observation (EO) satellites provide near real time, comprehensive, synoptic and multi-temporal coverage of inaccessible areas at frequent intervals, which is required support for a quick response and planning of emergency operations. Owing to their merits, satellite images have become an integral part of disaster management and are being extensively used globally for mapping, monitoring and damage assessment of extreme disaster events. During major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the unfavourable weather conditions, collapse of communication systems and inaccessibility to the area. Satellite images help in identifying the location of the disaster, its severity and the extent. The International Charter "Space and Major Disasters" has been the major sources of satellite data, in times of catastrophic disasters, due to availability of data from large number of sensors (with 15 organisations as signatories), which can be planned with the required temporal frequency and spectral range to cover a disaster event. During last three years, International Charter has been activated regularly, during major disasters in India. Satellite data from different sensors is obtained and was used for improving the frequency of observations, and extracting detailed information. This is used during floods in Assam (2012), floods in Uttarakhand (2013), cyclone Phailin (2013) and floods in Jammu and Kashmir (2014). The present paper discusses the role of International Charter in effective flood disaster management in India during recent past.

scholarly journals APPLICATION OF OFF-NADIR SATELLITE IMAGERY IN EARTHQUAKE DAMAGE ASSESSMENT USING OBJECT-BASED HOG FEATURE DESCRIPTOR

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 167-171
Author(s):  
S. Jabari ◽  
M. Krafczek
Keyword(s):  
Change Detection ◽  
Damage Detection ◽  
Disaster Management ◽  
Satellite Images ◽  
Feature Descriptor ◽  
Object Based ◽  
Before And After ◽  
Map Generation ◽  
Spectral Comparison ◽  
Damage Map

<p><strong>Abstract.</strong> One of the most crutial applications of very-high-resolution (VHR) satellite images is disaster management. In disaster management, time is of great importance. Therefore, it is vital to acquire satellite images as quickly as possible and benefit from automatic change detection to speed up the process. Automatic damage map generation is performed by overlaying the co-registered before and after images of the area of interest and, compring them to highlight the affected infrastructures. For speeding up image capture, satellites tilt their imaging sensor and take images from oblique angles. However, this kind of image acquisition causes severe geometric distortion in the images, which hinders image co-registration in automatic change detection. In this study, a Patch-Wise Co-Registration (PWCR) solution is used. In this algorithm, the before and after images are co-registered in a segment-by-segment manner. From the literature, this algorithm is followed by a spectral comparison to detect changes. However, due to the complicated structure of debris in damage detection applications, spectral comparison methods cannot perform well. In this work, we developed an object-based method using Histogram of Oriented Gradient descriptor to detect damges and compared our results to different existing spectral and textural change detection methods. The algorithm is tested on images from the 2010-Heidi earthquake, captured by DigitalGlobe. The achieved highly accurate results demonstrate the potential of using off-nadir remote sensing images for automatic urban damage detection possibly in early response systems as it speeds up the damage map generation by providing flexibility to utilize images taken from different anlges.</p>

scholarly journals A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 3 ◽  
Author(s):  
Muhammad Aamir ◽  
Yi-Fei Pu ◽  
Ziaur Rahman ◽  
Muhammad Tahir ◽  
Hamad Naeem ◽  
...  
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Satellite Images ◽  
Discrete Wavelet ◽  
Building Detection ◽  
Detection Scheme ◽  
Low Contrast ◽  
Line Segments ◽  
High Resolution Satellite Images ◽  
Wide Range

Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

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