Airport Runway Detection Based On ANN Algorithm

Page 1

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303

Airport Runway Detection Based On ANN Algorithm Abuthahir A

Mohana Arasi M

PG Scholar/Applied Electronics, Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam.

Assistant professor, Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam.

Abstract—Automatic detection of airports is especially essential, attributable to the strategic importance of those targets. during this paper, a detection methodology is planned for flying field runways. This methodology, that operates on massive optical satellite pictures, consists of a segmentation methodsupported textural properties, and a runway form detection stage. within the segmentation method, manynative textural optionsarea unit extracted. Since the most effective discriminative options for flying field runways cannot be trivially foreseen, the ANN algorithmic ruleis utilized as a feature selector over an oversized set of options. Moreover, the chosenoptions with corresponding weights willgivedata on the hidden characteristics of runways. The plannedalgorithmic rule is examined with experimental work employing a comprehensive knowledge set consisting of enormous and high resolution satellite pictures and thriving results area unit achieved. Keywords: Airport runway detection, Textural features, Segmentation, ANN algorithm.

I.

INTRODUCTION

Airports are important structures from both economical and military perspective. Economically, as fundamental cargo and passenger transportation stations, airports serve to attract and retain businesses with national and globalties. Therefore, air- ports are a major force in the local, regional ,national and global economy, becoming increasingly significant interms of financial reasons. The military airports,i.e. Airbases, are also critical strategic targets considering the importance of the aviation branch of a nation’s defence forces. Airbases are used for not only take-off and landing of crucial bomber and fighter units, butalsocon sequential support operations such as strategic and tactical airlift, combatair drop and medical evacuation, promoting the worth of airports .From this point of view, automatic detection of airports can provide vital intelligence to take well-timed military measures in a state of war. The technological improvements on both computational hardware and pattern recognition techniques made identification of airports an attain able objective. Besides, increasing number of countries that have their own satellites renders the problem even more attractive, by the supplied un biased data to investigate. These reasons form the motivation of this measures during a state of war. The technological enhancements on each process hardware and pattern recognition techniques created identification of airports a possible objective. project. From now of read, automatic detection of airports will offer important intelligence to require well-timed military Besides, increasing variety of nations that have their own satellites renders the matter even additional enticing, by the equipped unbiased information to research.

These reasons type the motivation of this paper. during this letter, field runway detection is undertaken by the ANN learning algorithmic rule [14] utilized on an oversized set of textural options. it's used to find the most effective discriminative options with corresponding weights, which might represent the real native characteristics of the runway texture that can't be intuitively identified. Additionally, Adaboost doesn't suffer from the curse of spatiality and an over sized process price for the extraction of intensive variety of options since it discovers that options area unit to be employed in the classification and that area unit to be eliminated by its feature choice property. This strategy relies upon ending as several options as doable and property

PROPOSED RUNWAY DETECTION ALGORITHM The proposed runway detection method basically consists of two main stages, which are binary classification of regions based on textural properties, and analysis of these regions based on shape. In the first stage a coarse segmentation is done on the satellite image, in order to find candidate regions for airport runway, based on the textural properties. This segmentation is a binary segmentation, where regions are labelled as either ―probably belongs to a runway‖ or ―probably does not belong to a runway‖. After this segmentation, only regions that possibly belong to a runway are considered and proceed to the second stage. In the second stage, a shape detection algorithm, which discovers long parallel line segments, is carried out on the ―possibly runway‖ regions. These long parallel lines are considered as the identification marks of the two long sides of the elongated rectangle shape of the runway.

132


Turn static files into dynamic content formats.

Create a flipbook
Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.
Airport Runway Detection Based On ANN Algorithm by International Journal for Trends in Engineering and Technology - Issuu