Machine vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Digital Image Processing
Introduction to Algorithms
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Hyperspectral Data Exploitation: Theory and Applications
Hyperspectral Data Exploitation: Theory and Applications
Image segmentation using automatic seeded region growing and instance-based learning
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Theory
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Remote sensing image segmentation by active queries
Pattern Recognition
Fusion of supervised and unsupervised learning for improved classification of hyperspectral images
Information Sciences: an International Journal
Target detection based on a dynamic subspace
Pattern Recognition
GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images
Knowledge-Based Systems
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A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a minimum spanning forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixelwise classification is performed, and the most reliable classified pixels are chosen as markers. Each classification-derived marker is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Furthermore, the classification map is refined using the results of a pixelwise classification and a majority voting within the spatially connected regions. Experimental results are presented for three hyperspectral airborne images. The use of different dissimilarity measures for the construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.