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Combining the multispectral rasterized data and the three-dimensional (3D) lidar point cloud has long been a hot topic in the remote sensing field. This facilitates not only target recognition, land-classification, but also understanding for the ecosystems and environment. To address this problem, the concept of novel multispectral lidar (MSL), which captures multispectral reflectance and accurate...
We expect to predict learners' learning situation in the real-time during one course processing — if they can get the certificate. We have collected the students' behavior log of the first eight weeks of “C + + program design” course from Edx platform to predict whether a student can eventually pass the course and obtain the certificate. An complete experimental process to achieve prediction has been...
Computer aided diagnosis (CAD) has been important more than ever for accurate diagnosis of liver tumors. The paper presents a novel image representation method for classifying normal livers and livers with tumors. It starts by capturing region of interesting (ROI) for individual livers, on which patches are extracted densely. Histogram of oriented gradients (HOG) and intensity are then extracted as...
It is difficult to extract effective scattering features or describe data using simple statistical distribution for classification of polarimetric SAR. Deep learning is effective in generating complex data model since it use network graph to model data. In this paper, a classification scheme is proposed based on deep learning algorithm, and a hierarchical structure is used to classify data based on...
Polarization ratio and co-polarized phase information are very important for polarimetric synthetic aperture radar (SAR) image interpretation, especially in the area where a single scattering mechanism is dominant. In this study, a new method including both the parameters for scattering characterization is proposed based on the extended Bragg scattering (X-Bragg) model. The theoretical analysis, which...
Marginal information is of great importance for classification. This paper presents a new nonparametric linear discriminant analysis method named Push-Pull marginal discriminant analysis (PPMDA) which takes full advantage of marginal information. For two-class cases, the idea of this method is to determine projection directions such that the marginal samples of one class are pushed away from the between-class...
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