Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Statistical pattern recognition in remote sensing
Pattern Recognition
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
A novel extended local-binary-pattern operator for texture analysis
Information Sciences: an International Journal
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Information Sciences: an International Journal
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
Information Sciences: an International Journal
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery
IEEE Transactions on Image Processing
A constraint propagation approach to structural model based image segmentation and recognition
Information Sciences: an International Journal
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
An approach to SWIR hyperspectral hand biometrics
Information Sciences: an International Journal
Multi-scale local binary pattern with filters for spoof fingerprint detection
Information Sciences: an International Journal
Hi-index | 0.07 |
We present a novel cascaded classification approach by exploiting various contexts on different levels for high resolution remote sensing (HRRS) images. The contexts mentioned in our article are defined according to objects from a set of regions resulting from segmentation. The cascaded procedure comprises three stages: (1) initializing the classification using the object's inner context (i.e., the gray constraints of different pixels in an object), (2) correcting the classification using the object's neighbor context (i.e., the characteristic constraints of different objects adjacent to the concerned object), and (3) refining classification using the object's scene context (i.e., the distribution constraint of different objects' labels and their feature vectors in the whole scene). The proposed algorithm has the following distinctions. First, it uses an object's neighbor context to bridge the gap between its inner context and its scene context because the latter two types of contexts have inevitable drawbacks when being used for classification alone. Second, it carries on a cascaded classification procedure in which the previous stage provides a better initial classification for the following stage, and the result is gradually refined by integrating different contexts. The effectiveness and practicability of the proposed algorithm is demonstrated through a set of completely experimental results and substantiated using quantitative criteria.