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

Volume 7 Issue 1 January - March 2019

Research Paper

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

B. Bersi Beulah*

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

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),1-10.

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, introduce a spatial-frequency visual saliency analysis algorithm 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, propose a saliency analysis strategy that is based on an edge feature preserving wavelet transform and high-frequency wavelet coefficient reconstruction to complete the preextraction of aircraft candidates from airport regions that are detected by the FLS and crudely extract as many aircraft candidates as possible for additional classification in detected airport regions. Then, utilize feature descriptors that are 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 aircraft are recognized. The experimental results indicate that the proposed method not only reliably and effectively detects targets in high-resolution broad-area remotesensing images but also produces more robust results in complex scenes.