A Survey on: Hyper Spectral Image Segmentation and Classification Using FODPSO

The Spatial analysis of image sensed and captured from a satellite provides less accurate information about a remote location. Hence analyzing spectral becomes essential. Hyper spectral images are one of the remotely sensed images, they are superior to multispectral images in providing spectral information. Detection of target is one of the significant requirements in many are assuc has military, agriculture etc. This paper gives the analysis of hyper spectral image segmentation using fuzzy C-Mean (FCM)clustering technique with FODPSO classifier algorithm. The 2D adaptive log filter is proposed to denoise the sensed and captured hyper spectral image in order to remove the speckle noise

A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery

In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques.PostPrin

Hyper Spectral Image Segmentation and Classification Using Least Square Clustering Based on FODPSO

The spatial analysis of the image detected and acquired by a satellite provides less accurate information on a remote location. Hyperspectral images are one of the images detected remotely, they are superior to multispectral images that provide spectral information. detailed information is one of the important requirements in many areas, such as military, agriculture, etc. The FODPSO classifier algorithm is used with the grouping technique of least squares for image segmentation. The 2D adaptive filter is proposed to eliminate the noise of the hyperspectral image detected and captured in order to eliminate the noise of the spot. Denoising the hyperspectral image (HSI) is an essential pre-processing step to improve the performance of subsequent applications

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Pengenalan Pola Berbasis Segmentasi Citra Menggunakan Algoritma Fuzzy C-Means Dan K-Means

oai:jti.aisyahuniversity.ac.id:article/3Segmentasi merupakan salah satu bagian penting dalam analisis citra, karena pada prosedur ini gambar/citra yang diinginkan akan dianalisis untuk proses yang lebih lanjut agar lebih mudah di analisis gunat ujuan selanjutnya, misalnya pada pengenalan pola.Segmentasi citra yang merupakan bagian dari analisis citra digunakan untuk membagi sebuah citra menjadi beberapa bagian dan mengambil sebagian objek yang diinginkan.Salah satu teknik dalam segmentasi citra adalah dengan clustering. Clustering adalah suatu usaha untuk melakukan pengelompokan data berdasarkan kelas dan merupakan metode mengelompokkan atau mempartisi data dalam suatu dataset.Segmentasi citra berbasis clustering pada penelitian ini menggunakan metode K-Means dan metode Fuzzy C Means. K-Means merupakan metode yang simple dan cepat perhitungannya, sedangkan Fuzzy C-Means merupakan algoritma yang populer digunakan dalam teknik Fuzzy Clustering.Penelitian ini untuk mengetahui metode yang paling optimal dalam melakukan segmentasi citra. Sebelum melakukan segmentasi terlebih dahulu menentukan ruang warna menggunakan CIELab. Identifikasi data uji menggunakan dua pendekatan, yaitu analisis bentuk dan analisis tekstur.Hasil pengujian menunjukan algoritma K-Means menghasilkan segmentasi untuk identifikasi yang lebih baik dari pada Fuzzy C Means karena menghasilkan nilai yang hampir sama atau mendekati dengan nilai ekstraksi ciri citra yang tersedia

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema

Unsupervised Retinal Blood Vessel Segmentation Technique using pdAPSO and Difference Image Methods for Detection of Diabetic Retinopathy

Retinal vessel segmentation is a practice that has the potential of enhancing accuracy in the diagnosis and timely prevention of illnesses that are related to blood vessels. Acute damage to the retinal vessel has been identified to be the main cause of blindness and impaired vision. A timely detection and control of these illnesses can greatly decrease the number of loss of sight cases. However, the manual protocol for such detection is laborious and although autonomous methods have been recommended, the accuracy of these methods is often unreliable. We propose the utilization of the Primal-Dual Asynchronous Particle Swarm Optimisation (pdAPSO) and differential image methods in addressing the drawbacks associated with segmentation of retinal vessels in this study. The fusion of pdAPSO and differential image (which focuses on the median filter) produced a significant enhancement in the segmentation of huge and miniscule retinal vessels. In addition, the method also decreased erroneous detection near the edge of the retinal (that is not sensitive to light). The results are favourable for the median filter when compared to mean filter and Gaussian filter. The accuracy rate of 0.9559 (with a specificity of sensitivity rate of 0.9855), and a sensitivity rate of 0.7218 were obtained when tested using the Digital Retinal Images for Vessel Extraction database. The above result is a pointer that our approach will help in detecting and diagnosing the damage done to the retinal and thereby preventing loss of sight

Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions