Dr. Dongsheng Liu Profile

Dr. Dongsheng Liu

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Publications (3)

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Proceedings Article | 23 January 2024 Paper

DSET-Net: deep self-embedded transformer network for hyperspectral unmixing

Xueer You, Yuanchao Su, Lianru Gao, Xu Sun, Pengfei Li, Dongsheng Liu

Proceedings Volume 12978, 129780J (2024) https://doi.org/10.1117/12.3019479

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Recently, transformer self-attention mechanisms have had significant advantages in the deep learning field, and it has been extensively used for natural language processing and video tracking. Furthermore, self-attention mechanisms have also been applied in hyperspectral unmixing. Although self-attention mechanisms are usually efficient and flexible tools, the original transformer might break the inner structure of data during learning, causing negative effects to unmixing. In this work, we employ transformer self-attention mechanisms to achieve a deep self-embedded transformer network(DSET-Net) for hyperspectral unmixing. The proposed DSET-Net adopts an autoencoder framework and achieves local and overall feature parameter sharing in the encoder through a 'Transformer in Transformer (TNT)' strategy. The DSET-Net preserves the spatial details of hyperspectral images and involves only one convolution operation in the encoder, substantially improving the learning performance. The effectiveness of the proposed method is evaluated by using real hyperspectral datasets. Our experimental results indicate that the newly proposed DSET-Net is very competitive compared with other state-of-the-art approaches.

Proceedings Article | 30 August 2023 Paper

Impervious surface extraction and spatiotemporal analysis in the main urban area of Zhengzhou

Xiaoping Zhang, Fang Lu, Dongsheng Liu, Yongyong Li, Ying Lv

Proceedings Volume 12797, 127971H (2023) https://doi.org/10.1117/12.3007445

KEYWORDS: Landsat, Remote sensing, Analytical research, Geography

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Increased coverage of impervious surfaces is an important measure of urban sprawl, and understanding spatiotemporal dynamics of impervious surfaces is vital for regional and local development. Combining vegetation-impervious-surfaces oil model and linear spectral unmixing model, this paper used the Landsat TM and OLI remote sensing data from 2009 to 2019 to study the temporal and spatial variation characteristics of the impervious surfaces of Zhengzhou, and discussed the main driving factors leading to urban expansion. We found that impervious surface growth in the main urban area of Zhengzhou City was obvious, and the area of impervious surfaces increased from 337.52 km² in 2009 to 464.93 km² in 2019, with an average expansion speed of 12.69 km² /year. The impervious surfaces pattern of the research area changed significantly, and the expansion of impervious surface showed a flaky development in all directions. From 2009 to 2014, Zhengzhou’s expansion was mainly concentrated in Hi-Tech Industrial Development Zone, Zhongyuan District, as well as Economic and Technological Development Zone. Most of the expansion between 2014 and 2019 was concentrated in Zhengdong New District and Huiji District. Physical geography, urban population, GDP and national policies were the main drivers affecting the expansion of impervious surfaces.

Proceedings Article | 26 October 2022 Poster + Presentation + Paper

Flood risk analysis and mapping in Henan Province using remotely sensed data and GIS techniques

Xiaoping Zhang, Yongyong Li, Xiaoqing Ma, Zehang Sun, Dongsheng Liu, Ying Lv

Proceedings Volume 12269, 122690L (2022) https://doi.org/10.1117/12.2643526

KEYWORDS: Floods, Geographic information systems, Hazard analysis, Analytical research, Remote sensing, Natural disasters

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Zhengzhou City suffered the strongest rainstorm in history in July 2021, floods and other secondary disasters have greatly endangered the property and safety of local residents in Henan Province, China. In this paper, the flood risk of Henan Province was analyzed from the three perspectives of hazard, exposure and vulnerability, and seven factors of rainfall, elevation, slope, distribution density of river network, population, GDP and arable land occupation ratio were selected to create a flood disaster risk evaluation index system. Remotely sensed data and GIS were used to analyze and map the influencing factors, and then the comprehensive risk results of flood disasters in Henan Province were obtained based on analytic hierarchy process (AHP) method. Results show that the higher-risk area and high-risk area mainly locate in cities of Xinyang, Luohe, Zhumadian, Zhoukou and Xuchang, as well as the north of Zhengzhou, accounting for 43% of the total area of Henan Province. High risk area is mainly caused by high hazard of the flat terrain and abundant rainfall. The lower-risk area is mainly distributed in Sanmenxia, Jiyuan and Anyang, and the western part of Luoyang, that locate in high-elevation areas, and the vulnerability index of GDP is low in these areas. The rest of the areas are medium-risk areas. This research provides the background information for disaster prevention and mitigation management in Henan Province.

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