Dr. Yangsheng You Profile

Dr. Yangsheng You

at Chongqing Institute of Meteorological Sciences

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Publications (2)

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Proceedings Article | 9 October 2018 Presentation + Paper

Scene classification of multisource remote sensing data with two-stream densely connected convolutional neural network

Zhen Wan, Ronghua Yang, Yangsheng You, Zhilin Cao, Xinan Fang

Proceedings Volume 10789, 107890S (2018) https://doi.org/10.1117/12.2501846

KEYWORDS: Feature extraction, Scene classification, Remote sensing, Convolutional neural networks

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Scene classification is a hot research topic in the geoscience and remote sensing (RS) community. Currently, the investigations conducted in RS domain mainly use single source data (e.g. multispectral imagery (MSI), hyperspectral imagery (HSI), or light detection and ranging (LiDAR), etc.). However, one of the RS data aforementioned merely provides one certain perspective of the complex scenes while the multisource data fusion can provide complementary and robust knowledge about the objects of interest. We aim at fusing the spectral– spatial information of the HSI and the spatial-elevation information of LiDAR data for scene classification. In this work, the densely connected convolutional neural network (DensNet), which connects all preceding layers to later layers in feed-forword manner, is employed to effectively extract and reuse heterogeneous features from HSI and LiDAR data. More specifically, a novel two-stream DensNet architecture is proposed, which builds an identical but separated DensNet stream for each data respectively. Then one of stream is utilized to extract the spectral-spatial features from HSI, the other is exploited to extract the spatial-elevation features of LiDAR data. Subsequently, the spectral–spatial–elevation features extracted in two streams are deeply fused within the fusion network which consists of two fully-connected layers for the final classification. Experimental results conducted on widely-used benchmark datasets show that the proposed architecture provides competitive performance in comparison with the state-of-the-art methods.

Proceedings Article | 16 October 2009 Paper

Study on quantitative model for relationship between displacement and precipitation for Laozaoping landslide

Yangsheng You, Yanghua Gao, Yunhui Tang, Yanying Chen

Proceedings Volume 7492, 74924W (2009) https://doi.org/10.1117/12.838361

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With the technology of GPS, we conduct precise deformation monitoring of the Laozaoping Landslide in Chongqing for a flood period. By virtue of the precipitation data close to the landslide in the monitoring period, we researched the quantitative relationship model between the landslide displacement and precipitation. In addition, we made researches on the impacts of precipitation distribution on displacement and advanced the Landslide-Causing Index (LCI). The researches show the model for displacement of Laozaoping landslide and precipitation is statistically significant; the precipitation distribution exerts more significant effect on displacement than the amount of precipitation; it is very hard to precisely estimate the displacement of landslide in terms of the precipitation and soil moisture only.

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