The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Deep learning is a new field in machine learning research. Convolution neural network is the most important factor in image recognition. This paper mainly focuses on the network design and parameter optimization of convolution neural network. This paper is first based on the traditional handwritten digital classification framework LeNet-5 to improve, and implements the test on the ten and twenty-five...
In a convolutional neural network (CNN), convolution calculation can account for about 90% of the total processing work. This paper presents the design of a convolution hardware accelerator (CHA) which can support efficient matrix multiplication to speed up the convolution calculation. In our experiment, when a RISC-V Rocket processor is used to simulate the operation of a CNN for image classification,...
Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
Classification of multisensor data provides potential advantages over a single sensor in accuracy. In this paper, deep bimodal autoencoders are proposed for classification of fusing synthetic aperture radar (SAR) and multispectral images. The proposed deep network based on autoencoders is trained to discover both independencies of each modality and correlations across the modalities. Specifically,...
Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM)...
Deep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build...
Convolutional neural networks (CNNs), widely studied in the domain of computer vision, are more recently finding application in the analysis of high-resolution aerial and satellite imagery. In this paper, we investigate a deep feature learning approach based on CNNs for the detection of informal settlements in Dar es Salaam, Tanzania. This information is vital for decision making and planning of upgrading...
Nowadays the CNN is widely used in practical applications for image classification task. However the design of the CNN model is very professional work and which is very difficult for ordinary users. Besides, even for experts of CNN, to select an optimal model for specific task may still need a lot of time (to train many different models). In order to solve this problem, we proposed an automated CNN...
ELM with kernels and MapReduce have an unparalleled advantage of other similar technologies, which attract widely attention in machine learning and distributed data processing communities respectively. In this paper, we combine the advantage of ELM with kernels and MapReduce, and propose a Distributed Extreme Learning Machine with kernels based on MapReduce framework (DK-ELMM),which makes full use...
The aim of scene semantic segmentation is to label each pixel with a class which it belongs to in high level cognition. State-of-art works mainly adapt convolutional neural networks originally designed for image classification to make dense prediction. However the inner structure of scene itself and its stuff is more flexible and variable, which is distinct from the objects in image classification...
Image processing tasks has found a new dimension with the improvement of learning feature representation from images using deep networks. Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement. For the last three decades, many...
Extreme learning machine, which has recently lead to gain popularity of single hidden layer feed-forward neural networks, provides a key solution for non-linear problems with least norm and least square solutions at a very low run time. In this work, it is intended to increase the success of hyperspectral image classification with using kernel extreme learning machine. For this purpose, a hybrid kernel...
In this paper, the authors investigated the main types of mammary dysplasia. In order to classify biomedical images, the researchers developed a basic model of convolutional neural network (CNN). Input parameters of the neural network to classify cytological and histological images were thoroughly researched and selected.
At present, collaborative representation based classification (CRC) is widely used in many pattern classification and recognition tasks. Meanwhile, spatial pyramid matching (SPM) method, which considers the spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper,...
In the hyperspectral image classification area, a few number of labeled samples is a bottleneck for the improvement of classification accuracy. In order to tackle this problem, multiple one-dimensional embedding interpolation (M1DEI) has been used for hyperspectral image classification and achieved promising results. Despite the success, the complexity of M1DEI prevents its practical application....
In complex visual recognition systems, feature fusion has become crucial to discriminate between a large number of classes. In particular, fusing high-level context information with image appearance models can be effective in object/scene recognition. To this end, we develop an auto-context modeling approach under the RKHS (Reproducing Kernel Hilbert Space) setting, wherein a series of supervised...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM),...
As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). However, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy, we propose a novel spectral-spatial information integrating scheme for pixel-wise kernel...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.