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Airbase Detection and Airship Recognition in High Spatial Resolution Remote Sensing Images

B. Bersi Beulah*

* Department of Electronics and Communication Engineering, PET Engineering College, Tirunelveli, Tamil Nadu, India.

Periodicity:January - March'2019
DOI : https://doi.org/10.26634/jdp.7.1.16264

Abstract

In the proposed work, two-layer visual saliency analysis model and Support Vector Machines (SVMs) are used for Airport detection and Aircraft Recognition. In the First Layer Saliency (FLS) model, a spatial-frequency visual saliency analysis algorithm has been introduced that is based on a CIE Lab color space to reduce the interference of backgrounds and efficiently detect well-defined airport regions in broad-area remote-sensing images. In the second layer saliency model, a saliency analysis strategy is proposed that is based on an edge feature preserving wavelet transform and highfrequency wavelet coefficient reconstruction to complete the pre- extraction of aircraft candidates from airport regions that are detected by the FLS and as many aircraft candidates are crudely extracted as possible for additional classification in detected airport regions. Then, feature descriptors are utilized based on a dense SIFT and Hu moment to accurately describe these features of the aircraft candidates. Finally, these object features are inputted to the SVM, and the aircrafts are recognized. The experimental results indicate that the proposed method not only reliably and effectively detects targets in high-resolution broad-area remote- sensing images, but also produces more robust results in complex scenes.

Keywords

First Layer Saliency (FLS), Support Vector Machines (SVM), Aircraft Recognition, Remote Sensing

How to Cite this Article?

Beulah, B. B. (2019). Airbase Detection and Airship Recognition in High Spatial Resolution Remote Sensing Images. i-manager’s Journal on Digital Signal Processing, 7(1),1-11. https://doi.org/10.26634/jdp.7.1.16264

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