Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Total ordering based on space filling curves for multivalued morphology
ISMM '98 Proceedings of the fourth international symposium on Mathematical morphology and its applications to image and signal processing
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Multivalued Image Segmentation Based on First Fundamental Form
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
An improved watershed algorithm based on efficient computation of shortest paths
Pattern Recognition
A comparative study on multivariate mathematical morphology
Pattern Recognition
Statistical pattern recognition in remote sensing
Pattern Recognition
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Morphological operators on the unit circle
IEEE Transactions on Image Processing
A morphological gradient approach to color edge detection
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification
IEEE Transactions on Image Processing
Region-based segmentation of 2D and 3D images with tissue-like P systems
Pattern Recognition Letters
A hybrid gradient for n-dimensional images through hyperspherical coordinates
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A stochastic gravitational approach to feature based color image segmentation
Engineering Applications of Artificial Intelligence
Spectral-spatial classification of hyperspectral imagery based on Random Forests
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Hyperspectral image segmentation through evolved cellular automata
Pattern Recognition Letters
GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images
Knowledge-Based Systems
Applications of Hybrid Extreme Rotation Forests for image segmentation
International Journal of Hybrid Intelligent Systems
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Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures.