Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A texture thesaurus for browsing large aerial photographs
Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Large-scale Satellite Image Browsing using Automatic Semantic Categorization
ICCVW '05 Proceedings of the Tenth IEEE International Conference on Computer Vision Workshops
Tree detection from aerial imagery
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Image Classification Using Subgraph Histogram Representation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A GPU implementation of a structural-similarity-based aerial-image classification
The Journal of Supercomputing
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The amount of remote sensed imagery that has become available by far surpasses the possibility of manual analysis. One of the most important tasks in the analysis of remote sensed images is land use classification. This task can be recast as semantic classification of remote sensed images. In this paper we evaluate classifiers for semantic classification of aerial images. The evaluated classifiers are based on Gabor and Gist descriptors which have been long established in image classification tasks. We use support vector machines and propose a kernel well suited for using with Gabor descriptors. These simple classifiers achieve correct classification rate of about 90% on two datasets. From these results follows that, in aerial image classification, simple classifiers give results comparable to more complex approaches, and the pursuit for more advanced solutions should continue having this in mind.