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

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.