Split-and-merge segmentation of aerial photographs
Computer Vision, Graphics, and Image Processing
The nature of statistical learning theory
The nature of statistical learning theory
Swarm intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Ant Colony Optimization
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Aggregation pheromone density based data clustering
Information Sciences: an International Journal
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Depth image enlargement using an evolutionary approach
Image Communication
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The landuse or land-cover map depicts the physical coverage of the Earth's terrestrial surface according to its use. Landuse map generation from remotely sensed images is one of the challenging tasks of remote sensing technology. In this article, motivated from group forming behavior of real ants, we have proposed two novel ant based (one supervised and one unsupervised) algorithms to automatically generate landuse map from multispectral remotely sensed images. Here supervised landuse map generation is treated as a classification task which requires some labeled patterns/pixels beforehand, whereas the unsupervised landuse map generation is treated as a clustering based image segmentation problem in the multispectral space. Investigations are carried out on four remotely sensed image data. Experimental results of the proposed algorithms are compared with corresponding popular state of the art techniques using various evaluation measures. Potentiality of the proposed algorithms are justified from the experimental outcome on a number of images.