Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1810015(2022)
Cloud-Type Recognition Based on Multiscale Features and Gradient Information
Aiming at the complicated problem of cloud image feature extraction method of all-sky imager, we propose a cloud-type classification model, that is, a dual-path gradient convolutional neural network (DGNet), by combining a double-line dense structure and gradient information to optimize the ability of the network to learn features of cloud images. The classification model is constructed using dual-thread parallel dense modules, and a gradient algorithm is applied to the feature maps. Experimental results show that compared with classic models, the accuracy of the proposed model improves significantly, reaching 67.00%. The main contributions of this study are as follows: the proposed model adopts a multithread and multiscale gradient dense module structure to reduce the loss of feature information; The gradient algorithm is used to fully extract the gradient change features of the cloud image to enhance the model’s accuracy for recognizing cloud species; A new data set of all-sky images is proposed, which contains 10 types of cloud images and 100 images of each type, accounting for 1000 images; Compared with the existing models, the proposed model shows the best accuracy, proving the feasibility of the proposed model.
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Lingjie Jin, Zhiwei Lin, Yu Hong. Cloud-Type Recognition Based on Multiscale Features and Gradient Information[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810015
Category: Image Processing
Received: Jul. 8, 2021
Accepted: Aug. 10, 2021
Published Online: Aug. 29, 2022
The Author Email: Lin Zhiwei (cwlin@fafu.edu.cn)