A novel unsupervised bee colony optimization (UBCO) method for remote sensing image classification:a case study in a heterogeneous marsh area

Unsupervised image classification is an important means to obtain land use/cover information in the field of remote sensing, since it does not require initial knowledge (training samples) for classification. Traditional methods such as k-means and ISODATA have limitations in solving this NP-hard unsupervised classification problem, mainly due to their strict assumptions about the data distribution. The bee colony optimization (BCO) is a new type of swarm intelligence, based upon which a simple and novel unsupervised bee colony optimization (UBCO) method is proposed for remote sensing image classification. UBCO possesses powerful exploitation and exploration capacities that are carried out by employed bees, onlookers and scouts. This enables the promising regions to be globally searched quickly and thoroughly, without becoming trapped on local optima. In addition, it has no restrictions on data distribution, and thus is especially suitable for handling complex remote sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR)—a typical inland wetland ecosystem in China, whose landscape is heterogeneous. The preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically significant better classification result (McNemar test) in comparison with traditional k-means (63.11%) and other intelligent clustering methods built on genetic algorithm (UGA, 71.49%), differential evolution (UDE, 77.57%) and particle swarm optimization (UPSO, 69.86%). The robustness and superiority of UBCO were also demonstrated from the two other study sites next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling to consistently find the optimal or nearly optimal global solution in image clustering, the UBCO is thus suggested as a robust method for unsupervised remote sensing image classification, especially in the case of heterogeneous areas

Accelerating sparse restricted Boltzmann machine training using non-Gaussianity measures

In recent years, sparse restricted Boltzmann machines have gained popularity as unsupervised feature extractors. Starting from the observation that their training process is biphasic, we investigate how it can be accelerated: by determining when it can be stopped based on the non-Gaussianity of the distribution of the model parameters, and by increasing the learning rate when the learnt filters have locked on to their preferred configurations. We evaluated our approach on the CIFAR-10, NORB and GTZAN datasets

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202