- 标题
- 摘要
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- 实验方案
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A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
摘要： Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
关键词： deep metric learning,determinantal point process (DPP),image classification,multiscale features,Convolutional neural network (CNN),hyperspectral image
更新于2025-09-23 15:23:52
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Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification
摘要： Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.
关键词： UAV,random forest classification,pavement health conditions,LiDAR,asphalt pavement distresses,multiscale features
更新于2025-09-19 17:15:36