Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set

Highlights

• We propose a new hybrid model for segmentation of SAR images.

• Neutrosophic set captures the textural information of SAR image more precisely.

• The proposed method can segment the SAR images containing speckle noise.

• I-ABC algorithm is used for the optimization of objective function.

Abstract

This paper proposes a novel synthetic aperture radar (SAR) image segmentation algorithm based on the neutrosophic set (NS) and improved artificial bee colony (I-ABC) algorithm. In this algorithm, threshold value estimation is considered as a search procedure that searches for a proper value in a grayscale interval. Therefore, I-ABC optimization algorithm is presented to search for the optimal threshold value. In order to get an efficient and powerful fitness function for I-ABC algorithm, the input SAR image is transformed into the NS domain. Then, a neutrosophic T and I subset images are obtained. A co-occurrence matrix based on the neutrosophic T and I subset images is constructed, and two-dimensional gray entropy function is described to serve as the fitness function of I-ABC algorithm. Finally, the optimal threshold value is quickly explored by the employed, onlookers and scouts bees in I-ABC algorithm. This paper contributes to SAR image segmentation in two aspects: (1) a hybrid model, having two different feature extraction methods, is proposed. (2) An optimal threshold value is automatically selected by maximizing the separability of the classes in gray level image by incorporating a simple and fast search strategy. The effectiveness of the proposed algorithm is demonstrated by application to real SAR images.

Introduction

Synthetic aperture radar (SAR) image segmentation is a significant part of SAR image analysis tasks. It ensures the entire structure of the image information and exposes useful information of SAR images. SAR images are used to define and interpret many objects in agriculture, urban design and military applications. In recent years, automated segmentation of SAR images has become a popular studying field [1], [2], [3], [4], [5], [6]. Generally speaking, segmentation techniques of SAR images can be divided into two categories: feature-based algorithms [7], [8], [9], [10], [11], [12] and model-based algorithms [13], [14]. Additionally, region-based segmentation methods have also been used [15], [16]. These methods reduce the computation complexity by working on regions instead of pixel neighborhood. Bai et al. [2] developed an edge detector which based on modified ratio and gradient of averages (MRGoA). According to them, a pixel is considered as an edge pixel only when both gradient and ratio of averages satisfy the desired threshold conditions. However, gradient is affected by the noise. Thus it is hard to get ideal results on fuzzy, discrete and noisy object edges. Ranjani and Thiruvengadam [5] proposed entropy-based MRGoA model (EB-MRGoA) for SAR image segmentation. In [7], a powerful segmentation model using the existing ratio of exponential weighted averages (ROEWA) edge detector is developed. In this method, a manual threshold strategy is used for the segmentation of image. But, if the SAR image has more than one class, estimation of its threshold is difficult. By developing the ROEWA method, multi-level SAR image segmentation method (MROEWA) is proposed [8]. Yu et al. [10] proposed a segmentation method which based on context and feature information of image, and named as Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS). This method uses super pixels as the operation units instead of pixels to reduce the influence of multiplicative speckle noises. Yu and Clausi [16] introduced region growing technique using semantics (IRGS and C-MLL) for SAR image segmentation.

Feature-based algorithms have the drawback of yielding images that are degraded by strong multiplicative speckle noise. Robust filtering is required to reduce the influence of the multiplicative speckle noise, which usually deteriorates the quality of segmentation of these algorithms. Model-based segmentation methods require mathematical foundation. They can extract or even enhance right object edges while removing speckle noise. These algorithms require an energy minimization process to find a satisfactory optimal solution for image [17]. For the energy minimization function, the split Bregman iteration is a proper choice [14]. Model-based methods have computational complexity. In general, model-based methods do not solve the issue of disconnected region segmentation and poor contrast in the SAR images.

This paper proposes a new segmentation method based on Improved Artificial Bee Colony (I-ABC) algorithm [18] and neutrosophic set [19], [20] for SAR images. I-ABC algorithm is used to optimize the two-dimensional gray entropy function and compute the global threshold value. The fitness functions of I-ABC algorithm based on two-dimensional gray entropy. The original ABC algorithm is a Swarm Intelligence (SI) optimization technique such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Artificial Fish Swarm (AFS) and based on the foraging behavior of honeybees [21], [22]. These nature-inspired algorithms are successfully applied to numeric function optimization, filtering noisy transcranial Doppler signal, binary optimization, vehicle routing problem and image processing [23], [24], [25], [26]. Neutrosophic set (NS) approach was suggested by Florentin Smarandache as a branch of philosophy dealing with the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra [19]. In this theory, every event has not only a truth degree, but also a falsity degree and an indeterminacy degree that have to be evaluated independently from each other [19]. Guo and Cheng gave an example about reviewing paper to explain how the NS works [20].

The rest of the paper is organized as follows. In the next section, we comprehensively describe the neutrosophic set approach, the original ABC algorithm and I-ABC algorithm. Section “Proposed approach” presents the proposed hybrid segmentation algorithm. In section “Experimental results and discussions”, experimental results and performance evaluation are presented in detailed. Noise-free images, noise images and well-known real SAR images used to evaluate the performance of the developed approach. In addition to this, the comparative evaluations of the segmentation results are discussed in terms of segmentation time, segmentation accuracy and convergence speed. Finally, some final remarks and conclusions are given in section “Conclusion”.