P
Peng Lei
Researcher at Beihang University
Publications - 8
Citations - 184
Peng Lei is an academic researcher from Beihang University. The author has contributed to research in topics: Convolutional neural network & Synthetic aperture radar. The author has an hindex of 5, co-authored 8 publications receiving 91 citations.
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Journal ArticleDOI
Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks
TL;DR: A dual stage coupled CNN architecture, named despeckling and classification coupled CNNs (DCC-CNNs), is proposed to distinguish multiple categories of ground targets in SAR images with strong and varying speckle to solve the noise robustness problem of CNN.
Book ChapterDOI
Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution
TL;DR: A deep residual convolutional neural network is proposed to increase the spatial resolution of hyperspectral image by minimizing the difference between the estimated image and the ground truth high resolution image.
Journal ArticleDOI
Acceleration of FPGA Based Convolutional Neural Network for Human Activity Classification Using Millimeter-Wave Radar
TL;DR: An acceleration method of the convolutional neural network (CNN) on the field-programmable gate array (FPGA) for the embedded application of the millimeter-wave (mmW) radar-based human activity classification shows that it maintains the high classification accuracy but also improves its execution speed, memory requirement, and power consumption.
Journal ArticleDOI
A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery
TL;DR: A ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN), which can outperform the conventional constant false-alarm rate technique and CNN-based models.
Journal ArticleDOI
A Joint Convolutional Neural Network for Simultaneous Despeckling and Classification of SAR Targets
TL;DR: The proposed joint convolutional neural network (J-CNN) for simultaneous despeckling and classification of SAR targets significantly outperforms other models under strong speckle noise condition but also has an efficient architecture with fewer weight parameters.