A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Texture image classification with discriminative neural networks

Texture provides an important cue for many computer vision applications, and texture image classification has been an active research area over the past years. Recently, deep learning techniques using convolutional neural networks(CNN) have emerged as the state-of-the-art: CNN-based features provide a significant performance improvement over previous handcrafted features. In this study, we demonstrate that we can further improve the discriminative power of CNN-based features and achieve more accurate classification of texture images. In particular, we have designed a discriminative neural network-based feature transformation(NFT) method, with which the CNN-based features are transformed to lower dimensionality descriptors based on an ensemble of neural networks optimized for the classification objective. For evaluation, we used three standard benchmark datasets(KTH-TIPS2, FMD, and DTD)for texture image classification. Our experimental results show enhanced classification performance over the state-of-the-art.

Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications.In particular,recognizing human activities with accurate results have become a topic of high interest.Although the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and conditions.In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld applications.In particular,two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components.The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions.In addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties.Moreover,a learning step via variational Bayesian setting is presented in a flexible way.The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form.Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

Large-scale scene real-time infrared simulation based on texture blending

Purpose-Infrared simulation plays an important role in small and affordable unmanned aerial vehicles.Its key and main goal is to get the infrared image of a specific target.Infrared physical model is established through a theoretical research,thus the temperature field is available.Then infrared image of a specific target can be simulated properly while taking atmosphere state and effect of infrared imaging system into account.For recent years,some research has been done in this field.Among them,the infrared simulation for large scale is still a key problem to be solved.In this passage,a method of classification based on texture blending is proposed and this method effectively solves the problem of classification of large number of images and increase the frame rate of large infrared scene rendering.The paper aims to discuss these issues.Design/methodology/approach-Mosart Atmospheric Tool(MAT)is used first to calculate data of sun radiance,skyshine radiance,path radiance,temperatures of different material which is an offline process.Then,shader in OGRE does final calculation to get simulation result and keeps a high frame rate.Considering this,the authors convert data in MAT file into textures which can be easily handled by shader.In shader responding,radiance can be indexed by information of material,vertex normal,eye and sun.Adding the effect of infrared imaging system,the final radiance distribution is obtained.At last,the authors get infrared scene by converting radiance to grayscale.Findings-In the fragment shader,fake infrared textures are used to look up temperature which can calculate radiance of itself and related radiance.Research limitations/implications-The radiance is transferred into grayscale image while considering effect of infrared imaging system.Originality/value-Simulation results show that a high frame rate can be reached while guaranteeing the fidelity.