GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images

Authors:
Stelios K. Mylonas;Dimitris G. Stavrakoudis;John B. Theocharis
Affiliations:
-;-;-
Venue:
Knowledge-Based Systems
Year:
2013

Citing 11
Cited 0

Hierarchy in Picture Segmentation: A Stepwise Optimization Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support-Vector Networks

Machine Learning
Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

Graphical Models and Image Processing
The Image Foresting Transform: Theory, Algorithms, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeded region growing: an extensive and comparative study

Pattern Recognition Letters
A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics

Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Segmentation and classification of hyperspectral images using watershed transformation

Pattern Recognition
Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation

Pattern Recognition Letters
Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image segmentation using evolutionary computation

IEEE Transactions on Evolutionary Computation
A region dissimilarity relation that combines feature-space and spatial information for color image segmentation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an object-based classification scheme for handling remotely sensed images. The method combines the results of a supervised pixel-based classifier with spatial information extracted from image segmentation. First, pixel-wise classification is implemented by a fuzzy output SVM classifier using spectral and textural features of pixels. This classification results to a set of fuzzy membership maps. Operating on this transformed space, a Genetic Sequential Image Segmentation (GeneSIS) algorithm is next developed to partition the image into homogeneous regions. GeneSIS follows a sequential object extraction approach, whereby at each iteration a single object is extracted by invoking a GA-based object extraction algorithm. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between three fuzzy components: coverage, consistency and smoothness. The final classification map is obtained automatically via segmentation, since each segment is extracted with its own class label. The validity of the proposed method is shown on the land cover classification of three different remote sensing images, with varying number of spectral bands (multispectral/hyperspectral), different spatial resolutions and ground truth cover types. The accuracy results of our approach are favorably compared with the ones obtained by other segmentation-based classification techniques.