Gholamreza Akbarizadeh

Bio: Gholamreza Akbarizadeh is an academic researcher from Shahid Chamran University of Ahvaz. The author has contributed to research in topics: Synthetic aperture radar & Image segmentation. The author has an hindex of 22, co-authored 63 publications receiving 1390 citations. Previous affiliations of Gholamreza Akbarizadeh include Iran University of Science and Technology.

A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

Abstract: In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.

Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier

Abstract: Ship detection on the SAR images for marine monitoring has a wide usage. SAR technology helps us to have a better monitoring over intended sections, without considering atmospheric conditions, or image shooting time. In recent years, with advancements in convolutional neural network (CNN), which is one of the well-known ways of deep learning, using image deep features has increased. Recently, usage of CNN for SAR image segmentation has been increased. Existence of clutter edge, multiple interfering targets, speckle and sea-level clutters makes false alarms and false detections on detector algorithms. In this letter, constant false alarm rate is used for object recognition. This algorithm, processes the image pixel by pixel, and based on statistical information of its neighbor pixels, detects the targeted pixels. Afterward, a neural network with hybrid algorithm of CNN and multilayer perceptron (CNN–MLP) is suggested for image classification. In this proposal, the algorithm is trained with real SAR images from Sentinel-1 and RADARSAT-2 satellites, and has a better performance on object classification than state of the art.

A Two-Phase Algorithm Based on Kurtosis Curvelet Energy and Unsupervised Spectral Regression for Segmentation of SAR Images

Abstract: Texture-based segmentation of synthetic aperture radar (SAR) image is a difficult task in remote sensing applications because it must address the problem of speckle noise. Several methods have been proposed for this purpose based on clustering, but suffer from long run times, computational complexity, and high-memory consumption. The proposed technique consists of two phases for SAR image segmentation. A new algorithm for parameter estimation based on curvelet coefficient energy (KCE) to design an optimum kernel function and an unsupervised spectral regression (USR) method have been proposed in phases 1 and 2, respectively, for SAR image segmentation. Eigen-decomposition is not required in USR, which decreases run times over other methods. The proposed algorithm uses a single-stage curvelet to extract the texture feature. Then, a new term is introduced based on the kurtosis feature value of the curvelet coefficients energy of the SAR image. Finally, the level set method is used to outline the boundaries between textures. Subimages are then extracted from the textures. After the Gabor filter is applied and the features are extracted, they are learnt using USR and clustered using a $k$ -means algorithm. It is demonstrated that the clustering results based on the learned features will be improved significantly. SAR image segmentation is performed using the $k$ -means after applying the Gabor filter bank and feature extraction. The results of segmentation are compared with Nystrom and parallel sparse spectral clustering (PSSC). The proposed method was shown to be more accurate and had a shorter run time than either Nystrom or PSSC.

Change detection in SAR images using deep belief network: a new training approach based on morphological images

Abstract: In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning-based method is presented for change detection in synthetic aperture radar (SAR) images. A Deep Belief Network (DBN) was employed as the deep architecture in the proposed method, and the training process of this network included unsupervised feature learning followed by supervised network fine-tuning. From a general perspective, the trained DBN produces a change detection map as the output. Studies on DBNs demonstrate that they do not produce ideal output without a proper dataset for training. Therefore, the proposed method in this study provided a dataset with an appropriate data volume and diversity for training the DBN using the input images and those obtained from applying the morphological operators on them. The great computational volume and the time-consuming nature of simulation are the drawbacks of deep learning-based algorithms. To overcome such disadvantages, a method was introduced to greatly reduce computations without compromising the performance of the trained DBN. Experimental results indicated that the proposed method had an acceptable implementation time in addition to its desirable performance and high accuracy.

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Abstract: Coastline extraction from synthetic aperture radar SAR data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low-and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets

Abstract: Human opinion cannot be restricted to yes or no as depicted by conventional fuzzy set (FS) and intuitionistic fuzzy set (IFS) but it can be yes, abstain, no and refusal as explained by picture fuzzy set (PFS). In this article, the concept of spherical fuzzy set (SFS) and T-spherical fuzzy set (T-SFS) is introduced as a generalization of FS, IFS and PFS. The novelty of SFS and T-SFS is shown by examples and graphical comparison with early established concepts. Some operations of SFSs and T-SFSs along with spherical fuzzy relations are defined, and related results are conferred. Medical diagnostics and decision-making problem are discussed in the environment of SFSs and T-SFSs as practical applications.

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Abstract: Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

Abstract: In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network

Abstract: High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.