Mr. Zheng Gong Profile

Zheng Gong

at Jilin Univ

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Publications (2)

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Proceedings Article | 1 August 2021 Poster + Paper

Ship target detection method for SAR image of FNLM filtering combined with faster R-CNN

Mingda Jiang, Lingjia Gu, Zheng Gong

Proceedings Volume 11829, 1182914 (2021) https://doi.org/10.1117/12.2592915

KEYWORDS: Target detection, Synthetic aperture radar, Image filtering, Image processing, Detection and tracking algorithms, Denoising, Image resolution

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Realizing the rapid detection of ship targets is of great significance both in commercial shipping activities and in modern warfare. Compared with visible light remote sensing images, synthetic aperture radar (SAR) has good resolution characteristics for metal targets, making ship targets more clearly visible in high-resolution SAR images. However, there are often obvious coherent bright spots and sea clutter noise in high-resolution SAR images used for ship detection, which seriously affects the detection accuracy. This paper proposed a detection method that uses fast non-local means (FNLM) filter and deep learning methods. Using high-resolution SAR images as the data source, FNLM and deep learning methods are used to realize the detection of ships quickly and accurately. First, the FNLM filter was used to preprocess highresolution SAR images to reduce overall image noise while enhancing target definition and feature details; Then, the Faster R-CNN algorithm was used to conduct model training on large-scale SAR data sets, to extract detection features, to improve the convergence accuracy of the network, and to achieve automatic and rapid detection of ships on the sea. Experimental results showed that this method had good robust characteristics and target detection accuracy, and the average accuracy of ship target detection reaches more than 90%. In the case of severe sea clutter interference, it still had a better detection effect.

Proceedings Article | 20 August 2020 Poster + Paper

Forest classification based on GF-5 hyperspectral remote sensing data in Northeast China

Zheng Gong, Lingjia Gu, Ruizhi Ren, Shuting Yang

Proceedings Volume 11501, 115011E (2020) https://doi.org/10.1117/12.2566127

KEYWORDS: Principal component analysis, Image classification, Hyperspectral imaging, Remote sensing, Feature extraction, Vegetation, Machine learning, Image processing

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Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology and spectral technology. It can obtain continuous and narrow band image data with high spectral resolution. Therefore, hyperspectral remote sensing has great potential in the identification of ground features and the classification of vegetation types. In this paper, GF-5 data was used as training data to classify forest types in Northeast China. Firstly, the water absorption bands and some noise bands were removed from the GF-5 hyperspectral image. Furthermore, the bands were grouped according to their correlation, and principal component analysis (PCA) was performed on each group of bands. According to the band index, the bands with better quality were extracted from each group and combined with the bands obtained by PCA to reduce the dimension of hyperspectral data. Then the Convolutional Neural Network (CNN) was used to extract the features of the processed image, and the extracted features were input into the support vector machine (SVM) classifier to obtain the forest vegetation type. By combining CNN and SVM, a hyperspectral forest classification model based on CNN-SVM fusion is constructed. The experimental results show that the method proposed in this paper performs best in forest type classification accuracy. The overall classification accuracy can reach 88.67%, and the Kappa coefficient can reach 0.84.

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