A Spatial Thresholding Method for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Retrieval
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadowed sets: representing and processing fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Improving feature space based image segmentation via density modification
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Agriculture satellite image segmentation using a modified artificial Hopfield neural network
Computers in Human Behavior
Segmentation of color images using a linguistic 2-tuples model
Information Sciences: an International Journal
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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Satellite images often require segmentation in the presence of uncertainty, caused due to factors like environmental conditions, poor resolution and poor illumination. Since any subsequent image analysis depends on the quality of such segmentation, one has to obtain an efficient algorithm for the purpose. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper we map the newly developed shadowed clustering algorithm to the problem of segmenting remotely sensed images. It is observed that shadowed clustering can efficiently handle overlapping among segments while modeling uncertainty among the boundaries. Unlike rough clustering, here the choice of user-defined parameters is fully eliminated. The number of segments is automatically optimized in terms of validity indices. The algorithm is robust in the presence of outliers. The superiority of the system is demonstrated in segmenting a synthetic image, along with land cover types from the Indian Remote Sensing (IRS) images of the cities of Mumbai and Kolkata and the SPOT image around Kolkata. The algorithm is found to efficiently and accurately extract the different homogeneous regions in the presence of uncertainty. The results are analyzed both qualitatively and quantitatively.