Aircraft Detection by Deep Belief Nets
- 1 November 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2013 2nd IAPR Asian Conference on Pattern Recognition
Abstract
Aircraft detection is a difficult task in high-resolution remote sensing images, due to the variable sizes, colors, orientations and complex backgrounds. In this paper, an effective aircraft detection method is proposed which exactly locates the object by outputting its geometric center, orientation, position. To reduce the influence of background, multi-images including gradient image and gray thresholding images of the object were input to a Deep Belief Net (DBN), which was pre-trained first to learn features and later fine-tuned by back-propagation to yield a robust detector. Experimental results show that DBNs can detecte the tiny blurred aircrafts correctly in many difficult airport images, DBNs outperform the traditional Feature Classifier methods in robustness and accuracy, and the multi-images help improve the detection precision of DBN than using only single-image.Keywords
This publication has 20 references indexed in Scilit:
- Aircraft Recognition in High-Resolution Satellite Images Using Coarse-to-Fine Shape PriorIEEE Geoscience and Remote Sensing Letters, 2012
- Acoustic Modeling Using Deep Belief NetworksIEEE Transactions on Audio, Speech, and Language Processing, 2011
- Vehicle Detection Using Partial Least SquaresIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- On-line boosting-based car detection from aerial imagesISPRS Journal of Photogrammetry and Remote Sensing, 2008
- On the Orientability of ShapesIEEE Transactions on Image Processing, 2006
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Robust wide-baseline stereo from maximally stable extremal regionsImage and Vision Computing, 2004
- Multiresolution gray-scale and rotation invariant texture classification with local binary patternsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002