Mr. Qinglin Tian Profile

Qinglin Tian

at CUGB

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Proceedings Article | 12 March 2021 Paper

Application of hyperspectral images in digital record of drill cores

Xuejiao Chen, Wei Pan, Qinglin Tian, Chuan Zhang

Proceedings Volume 11763, 1176352 (2021) https://doi.org/10.1117/12.2587147

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Drill core is very important for researchers to study lithospheric structure. However, how to save cores in good condition as well as how to satisfy the need of studying drill cores anytime and anywhere as we want are big questions. Thus, numerical images of drill cores are very necessary. Hyperspectral images have lots of advantages, for they can provide both true color images and hyperspectral messages. True color images can be used for direct observation and study. And hyperspectral messages can be used for mineral identification. In practical use, there are distortions in the scanned hyperspectral images, so correction method is in great need. A geometric and luminance correction method is raised in this paper, and results show that the correction method works well. For the sake of observation, three-dimension display is more convenient. This paper provides an implementation method of 3D display. At last, this paper provides a method for extracting mineral alteration data. Through the methods above, hyperspectral images are useful and feasible in the digital record of drill cores.

Proceedings Article | 12 March 2021 Paper

Dense feature pyramid fusion deep network for building segmentation in remote sensing image

Yingjun Zhao, Kai Qin, Qinglin Tian, Yao Li, Xuejiao Chen

Proceedings Volume 11763, 1176351 (2021) https://doi.org/10.1117/12.2587144

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It is difficult to achieve detailed segmentation since the building size varies in high-resolution remote sensing images, especially for small buildings. To address these problems, a dense feature pyramid fusion deep network is proposed in this study. First, we built an encoder-decoder structure, and combine attention mechanism and atrous convolution to improve the feature extraction results in the encoder. Second, the pyramid pooling module is selected to extract the multi-scale features from different levels. Finally, dense feature pyramid is adopted in the decoder to fuse multi-level and multi-scale features to obtain the final segmentation results. Experiments on Inria Aerial Image Labeling Dataset show that our method achieves competitive performance compared with other classical semantic segmentation networks.

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