A self-paced learning algorithm for change detection in synthetic aperture radar images

Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness

Color Image Segmentation Using Generalized Inverted Finite Mixture Models By Integrating Spatial Information

In computer vision, image segmentation plays foundational role. Innumerable techniques, such as active contour, graph-cut-based, model-based, machine learning, and clustering-based methods have been proposed for tackling the image segmentation problem. But, none of them is universally applicable. Thus, the hunt for optimized and robust models for image segmentation is still under-process and also an open question. The challenges faced in image segmentation are the integration of spatial information, finding the exact number of clusters (M), and to segment the image smoothly without any inaccuracy specially in the presence of noise, a complex background, low contrast and, inhomogeneous intensity. The use of finite mixture model (FMMs) for image segmentation is very popular approach in the field of computer vision. The application of image segmentation using FMM ranges from automatic number plate recognition, content-based image retrieval, texture recognition, facial recognition, satellite imagery etc. Image segmentation using FMM undergoes some problems. FMM-based image segmentation considers neither spatial correlation among the peer pixels nor the prior knowledge that the adjacent pixels are most likely belong to the same cluster. Also, color images are sensitive to illumination and noise. To overcome these limitations, we have used three different methods for integrating spatial information with FMM. First method uses the prior knowledge of M. In second method, we have used Markov Random Field (MRF). Lastly, in third, we have used weighted geometric and arithmetic mean template. We have implemented these methods with inverted Dirichlet mixture model (IDMM), generalized inverted Dirichlet mixture model (GIDMM) and inverted Beta Liouville mixture model (IBLMM). For experimentation, the Berkeley 500 (BSD500) and MIT's Computational Visual Cognition Laboratory (CVCL) datasets are employed. Furthermore, to compare the image segmentation results, the outputs of IDMM, GIDMM, and IBLMM are compared with each other, using segmentation performance evaluation metrics

Обработка радиолокационных изображений: монография

Книга посвящена решению теоретических и практических проблем обнаружения, измерения параметров и классификации пространственно-распределённых целей (ПРЦ) по их радиолокационным изображениям (РЛИ), формируемым в многопозиционной системе наблюдения, реализованной группой космических аппаратов. В книге подробно рассмотрены методы синтеза и анализа алгоритмов классификации ПРЦ, алгоритмы оценки параметров РЛИ, алгоритмы классификации с использованием нейронных сетей, частично-когерентных РЛС, алгоритмы формирования РЛИ движущихся объектов, методы фильтрации спекл-шума, методы анализа помехоустойчивости, методы геокоррекции формируемых РЛИ. Книга представляет интерес для специалистов, студентов и аспирантов, работающих в области разработки современных радиотехнических систем военного и гражданского назначения

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field