Computer Vision, Graphics, and Image Processing
A new method of classification for Landsat data using the `watershed' algorithm
Pattern Recognition Letters - Special Colour Issue
Pattern Spectrum and Multiscale Shape Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Computer and Robot Vision
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery
Computers & Geosciences
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
A Hybrid Approach to Land Cover Classification from Multi Spectral Images
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A neutrosophic approach to image segmentation based on watershed method
Signal Processing
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Watershed transformation in mathematical morphology is a powerful morphological tool for image segmentation that is usually defined for greyscale images and applied to the gradient magnitude of an image. This paper presents an extension of the watershed algorithm for multispectral image segmentation. A vector-based morphological approach is proposed to compute gradient magnitude from multispectral imagery, which is then input into watershed transformation for image segmentation. The gradient magnitude is obtained at multiple scales. After an automatic elimination of local irrelevant minima, a watershed transformation is applied to segment the image. The segmentation results were evaluated and compared with other multispectral image segmentation methods, in terms of visual inspection, and object-based image classification using high resolution multispectral images. The experimental results indicate that the proposed method can produce accurate segmentation results and higher classification accuracy, if the scales and contrast parameter are appropriately selected in the gradient computation and subsequent local minima elimination. The proposed method shows encouraging results and can be used for segmentation of high resolution multispectral imagery and object based classification.