As the basic units of urban areas, functional zones are essential for city planning and management, but functional-zone maps are hardly available in most cities, as traditional urban investigations focus mainly on land-cover objects instead of functional zones. As a result, an automatic/semi-automatic method for mapping urban functional zones is highly required. Hierarchical semantic cognition (HSC) is presented in this study, and serves as a general cognition structure for recognizing urban functional zones. Unlike traditional classification methods, the HSC relies on geographic cognition and considers four semantic layers, i.e., visual features, object categories, spatial object patterns, and zone functions, as well as their hierarchical relations. Here, we used HSC to classify functional zones in Beijing with a very-high-resolution (VHR) satellite image and point-of-interest (POI) data. Experimental results indicate that this method can produce more accurate results than Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA) with a larger overall accuracy of 90.8%. Additionally, the contributions of diverse semantic layers are quantified: the object-category layer is the most important and makes 54% contribution to functional-zone classification; while, other semantic layers are less important but their contributions cannot be ignored. Consequently, the presented HSC is effective in classifying urban functional zones, and can further support urban planning and management.

Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing ...

Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

We improve dictionary learning algorithm for vector quantization of ECG.The algorithm is employed to extract feature of ECG.The algorithm can avoid interference from dirty data.The algorithm is capable of increasing classification accuracy.An initial cluster centers selecting method is utilized to speed up the algorithm. Vector quantization(VQ) can perform efficient feature extraction from electrocardiogram (ECG) with the advantages of dimensionality reduction and accuracy increase. However, the existing dictionary learning algorithms for vector quantization are sensitive to dirty data, which compromises the classification accuracy. To tackle the problem, we propose a novel dictionary learning algorithm that employs k-medoids cluster optimized by k-means++ and builds dictionaries by searching and using representative samples, which can avoid the interference of dirty data, and thus boost the classification performance of ECG systems based on vector quantization features. We apply our algorithm to vector quantization feature extraction for ECG beats classification, and compare it with popular features such as sampling point feature, fast Fourier transform feature, discrete wavelet transform feature, and with our previous beats vector quantization feature. The results show that the proposed method yields the highest accuracy and is capable of reducing the computational complexity of ECG beats classification system. The proposed dictionary learning algorithm provides more efficient encoding for ECG beats, and can improve ECG classification systems based on encoded feature.

Dictionary learning for VQ feature extraction in ECG beats classification

A novel topic feature for image scene classification

The invention relates to a feature extraction method for image scene recognition. The feature extraction method comprises the first step of mining information in a group of training images of which the classes are known and the second step of recognizing test images to be recognized. The first step comprises the sub-steps of preprocessing the images, extracting target features of the images, decreasing the dimensions of the target features, executing the LDA model training algorithm, generating scene environment features of the training images, carrying out feature combination and executing the SVM training algorithm. The second step comprises the sub-steps of preprocessing the testing images, generating code words of the testing images, generating scene environment features of the testing images, extracting target features of the testing images, decreasing the dimensions of the target features of the testing images, carrying out feature combination on the testing images and generating image classes through a trained SVM classifier. By means of the feature extraction method, the calculation amount of an existing method is deceased, the application range is expanded, and the recognition accuracy is improved.

Feature extraction method for image scene recognition

Object Bank (OB) is a high-level image representation encoding semantic and spacial information, and has superior performance in scene classification tasks. However, the dimensionality of OB feature is high, which demands massive computation. Existing dimensionality reduction methods for OB are incapable of achieving both high classification accuracy and substantial dimensionality reduction simultaneously. In order to solve this problem, we propose a threshold value filter pooling method to avoid noise accumulation in histogram-pooling and represent more useful information than max-pooling. We also propose a Matthew effect normalization method to highlight the useful information, and thus boost the performance of OB-based image scene classification. Finally, we apply these two methods in a dimensionality reduction framework to simplify OB representation and construct more proper descriptors, and thus achieve both dimensionality reduction and classification accuracy increase. We evaluated our framework on three real-world datasets, namely, event dataset UIUC-Sports, natural scene dataset LabelMe, and mixture dataset 15-Scenes. The classification results demonstrate that our framework not only obtains accuracies similar to or higher than the original OB representation, but also reduces the dimensionality significantly. The computational complexity analysis shows that it can reduce the time complexity of classification. Therefore, our framework can improve OB-based image scene classification through both computational complexity reduction and accuracy increase.

Mujun Zang

Papers

Dictionary learning for VQ feature extraction in ECG beats classification

A novel topic feature for image scene classification

Feature extraction method for image scene recognition

A pooled Object Bank descriptor for image scene classification

Electrocardiogram characteristic extraction method