Aerial Scene Recognition using Efficient Sparse Representation
- ORNL
Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1057503
- Resource Relation:
- Conference: Eigth Indian Conference on Computer Vision, Graphics and Image Processing, Mumbai, India, 20121216, 20121219
- Country of Publication:
- United States
- Language:
- English
Similar Records
Spatial Feature Evaluation for Aerial Scene Analysis
Sparse coding of pathology slides compared to transfer learning with deep neural networks