A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images

Stelios Mylonas,Paris Mastorocostas,Dimitris Stavrakoudis,John Theocharis

doi:10.3390/rs70302474

Abstract

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms.

Highlights

In recent years, the growing development and availability of satellite imagery with high spectral and spatial resolution (HSSR), poses new challenges in the field of land cover classification
Genetic Sequential Image Segmentation (GeneSIS) was coded in C++ and all experiments were conducted on an Intel Core i5-4670 at 3.4 GHz
In order to alleviate the stochastic effect of GeneSIS, we introduce in this paper two segmentation fusion schemes (Section 4), both operating on the watershed basis

Summary

Introduction

The growing development and availability of satellite imagery with high spectral and spatial resolution (HSSR), poses new challenges in the field of land cover classification. An attractive method recently receiving considerable attention is the incorporation of spatial information to improve the classification results obtained by traditional pixel-based classifiers. One way to achieve this goal is to extract contextual information from fixed-window neighborhoods around pixels and incorporate it into their feature vector of spectral values. The drawback of this method is that it raises the issue of scale selection, due to the existence of structures of different sizes within the image. A more effective alternative for integrating spatial information is to perform image segmentation. Segmentation is the partitioning of the image into disjointed regions so that each region is connected and homogeneous with respect to some homogeneity criterion of interest

Methods

Results

Conclusion