Mr. Jiawei Shi Profile

Jiawei Shi

at Beihang Univ

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Publications (2)

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Proceedings Article | 12 September 2021 Presentation + Paper

Deep-learning-based remote sensing video super-resolution for Jilin-1 satellite

Ning Zhao, Jiawei Shi, Pengrui Wang, Zhiguo Jiang, Haopeng Zhang

Proceedings Volume 11862, 118620E (2021) https://doi.org/10.1117/12.2599926

KEYWORDS: Optical flow, Super resolution, Video, Optical networks, Remote sensing, Image fusion, Convolution, Satellites, Motion estimation, Visualization

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Due to far imaging distance and relatively harsh imaging conditions, the spatial resolution of remote sensing data are relatively low. Images/videos super-resolution is of great significance to effectively improve the spatial resolution and visual effect of remote sensing data. In this paper, we propose a deep-learning-based video super-resolution method for Jilin-1 remote sensing satellite. We use explicit motion compensation method by calculating the optical flow through the optical flow estimation network and compensating the motion of the image through warp operation. After obtaining the multi-frame images after motion compensation, it is necessary to use multi-frame image fusion for super-resolution reconstruction. We performed super-resolution experiments with scale factor 4 on Jilin-1 video dataset. In order to explore suitable fusion method, we compared two kinds of image fusion methods in the super-resolution network, i.e. concatenation by channel and 3D convolution, without motion compensation. Experimental results show that 3D convolution achieves better super-resolution performance, and video super-resolution result is better than the compared single image super-resolution method. We also performed experiments with motion compensation by optical flow estimation network. Experimental results show that the difference between the image after motion compensation and the reference frame becomes smaller. This indicates that the explicit motion compensation method can compensate the difference between the frames due to the target motion to a certain extent.

Proceedings Article | 7 October 2019 Paper

Crop classification based on lightened convolutional neural networks in multispectral images

Jiawei Shi, Haopeng Zhang, Zhiguo Jiang, Gang Meng

Proceedings Volume 11155, 111552A (2019) https://doi.org/10.1117/12.2532747

KEYWORDS: Image classification, Mining, Multispectral imaging, Convolutional neural networks, Remote sensing, Statistical analysis, Data modeling, Visualization, Convolution, Network architectures

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Crop classification is a representative problem in multispectral remote sensing image (RSI) classification, and has significance in country food security, ecological security, production estimate, crop growth supervision, and so on. It has attracted increasing attention of many researchers around the world especially after the development of convolutional neural networks (CNN). General CNN-based multispectral RSI classification methods may be not suitable for labeled samples with limited numbers and areas. Other pixel-based classification methods are always affected by noise and ignore spatial information. Focusing on these problems, this paper presents an approach based on lightened CNN for crop classification with a small number of tiny size labeled samples in multispectral images. The contribution of this work is to construct a lightened CNN model for crop classification with small samples in multispectral image. It avoids overfitting of deep CNN and reduces the requirement for the size of training samples. We adopt two-layer fully convolutional network (FCN) to extract features. The first layer uses a convolutional kernel of size 1 and outputs 16-band feature map to obtain spectral band information. Spatial information is extracted in the sequential layer using convolutional kernel of size 3, step 1 and padding 1. Thus the feature map after FCN and the labeled area have the same size. Finally, we use a fully connected layer and a softmax classifier for classification. Our experiment was conducted on 8-band multispectral image of size 50362-by-17810 pixels. There are 5 classes in the multispectral image, namely rice, soy, corn, non-crop, and uncertainty. The experimental result which achieves 86.28% accuracy indicates the good performance of our network for crop classification in multispectral RSIs.

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