Shouhang Du

China University of Mining and Technology

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Co-Authors

Jianghe Xing

China University of Mining and Technology

Shihong Du

Peking University

Jun Li

Anhui Science and Technology University

Chengye Zhang

China University of Mining and Technology

Xiuyuan Zhang

Peking University

Wenbin Sun

China University of Mining and Technology

Wei Li

Beijing Institute of Technology

Xiongwu Xiao

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Su Cao

China University of Mining and Technology

Yurong Zheng

Southern Medical University

Cooperative Institutions

China University of Mining and Technology

Peking University

Beijing Normal University

Chinese Academy of Sciences

State Key Laboratory of Remote Sensing Science

Huazhong University of Science and Technology

Chinese Academy of Surveying and Mapping

Ministry of Natural Resources

Beihang University

Wuhan University

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Research Field

LUMNet: Land Use Knowledge Guided Multiscale Network for Height Estimation From Single Remote Sensing Images

IEEE Geoscience and Remote Sensing Letters (2024)

Shouhang Du Jianghe Xing Shaoyu Wang Xiongwu Xiao Jun Li

For estimating ground object height from single remote sensing images, this study proposes a land use knowledge guided multiscale height estimation network (LUMNet), which takes single image and land use data as inputs and produces an estimated height map as output. First, in the encoder part, the visual geometry group network (VGGNet) is used to extract multilevel deep semantic features from images. Second, the features of the encoder part are fused with those of the decoder part through skip connection with land use knowledge attention weight and Dense Atrous Spatial Pyramid Pooling (DenseASPP). Third, in the decoder part, the features are decoded using upsampling and convolution, and a joint loss function is constructed to supervise network training. Experiments show that the proposed method achieves the best visualizations and quantitative evaluation results among all the tested methods. For the Vaihingen dataset, the RMSE , MAE , and ZNCC of LUMNet are 1.523, 0.969, and 0.908, respectively. For the Potsdam dataset, these are 2.158, 1.222, and 0.871, respectively. The source code of LUMNet has been made public at the following link: https://figshare.com/s/eaf206879ade35a61f88.

10.1109/lgrs.2024.3374526

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STMNet: Scene Classification-Assisted and Texture Feature-Enhanced Multiscale Network for Large-Scale Urban Informal Settlement Extraction From Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024)

Shouhang Du Jianghe Xing Shaoyu Wang Liguang Wei Yirui Zhang

Automatic urban informal settlement (UIS) extraction based on high-resolution remote sensing image (HRI) is of great significance for urban planning and management. This study proposes a scene classification-assisted and texture feature-enhanced multiscale network (STMNet) for UIS extraction. First, STMNet takes HRI and computed handcrafted texture feature (HTF) as input. Second, it employs a pseudo-siamese network to extract multidimensional deep features from HRI and HTF, respectively. In addition, a feature attention fusion module is constructed to fuse the aforementioned features. Finally, skip connection and feature decoder are utilized to obtain UIS extraction results. In detail, considering the sparse and dispersed distribution of UIS, a scene information aggregation and classification module is constructed to determine whether the input image patch contains UIS. For the characteristics of high spatial heterogeneity and various shapes and scales of UIS, an improved atrous spatial pyramid pooling is presented to extract multiscale and multireceptive field features. An edge loss function is applied during network training to minimize errors in the edge regions of UIS. The effectiveness of STMNet is tested on a self-produced UIS extraction dataset and the publicly available UIS-Shenzhen dataset. Quantitative results demonstrate that STMNet achieved the best performance in terms of

$\text{fa}$

$F1$

, and

$\text{IoU}$

. The

$\text{mr}$

is slightly higher than that of MAResU-Net and UisNet. In addition, STMNet achieved the best visual interpretation results and the fastest inference speed on the self-produced UIS extraction dataset.

Feature (linguistics)

Pooling

10.1109/jstars.2024.3432200

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Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023)

Congcong Wang Shouhang Du Wenbin Sun Deqin Fan

Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pre-training can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network which is a change detection-like architecture is trained to extract multi-level fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck (VIB) theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.

Robustness

Feature Learning

10.1109/jstars.2023.3288294

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MGLI-Former: a multi-scale and global-local information interactive attention transformer for urban shantytown extraction

International Journal of Digital Earth (2024)

Shouhang Du Shaoyu Wang Yuhao Hua Peng Shu Feiwei Qin

Shantytowns, characterized by poor living conditions and simple houses, necessitate efficient extraction and analysis for urban planning. This paper proposes a multi-scale and global-local information interactive attention transformer (MGLI-Former) for shantytown extraction from high-resolution remote sensing images. First, the multi-level feature fusion block (MLFFB) integrates neighborhood encoding features to prevent the loss of small-target shantytowns. Second, joint global and local information transformer blocks (JGLB) effectively combine global and local features. Finally, boundary and feature joint optimization loss (BF-Loss) refines the output by edges and high-level semantics. Experiments in Beijing and Shanghai demonstrate the MGLI-Former achieved optimal visual and quantitative extraction evaluations. The F1-score, IoU, Precision, and Recall are 86.92%, 76.87%, 86.84%, 87.01% and 72.33%, 56.66%, 69.29%, 75.65%, respectively. Furthermore, the use of UIS-Shenzhen datasets and fine-tuning experiments with mixed datasets further validate the robustness and generalization capabilities of MGLI-Former. Moreover, spatial and landscape patterns of shantytowns in Beijing and Shanghai reveal: (1) Beijing's shantytowns radiate uniformly from the old city center, whereas Shanghai exhibits a multi-core diffusion pattern. (2) Shanghai's shantytown distribution is clustered, while Beijing's shantytown distribution is more uniform. MGLI-Former demonstrates the potential for extracting shantytowns and has significant urban planning and management implications.

10.1080/17538947.2024.2432522

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Estimation of canopy height based on multi-source remote sensing data using forest structure aided sample selection

International Journal of Remote Sensing (2024)

Yinpeng Zhao Shouhang Du Kangning Li Jinbao Jiang Qiyu Guo

Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps.

Tree canopy

Sample (material)

10.1080/01431161.2024.2326537

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Intra-annual land cover mapping and dynamics analysis with dense satellite image time series: a spatiotemporal cube based spatiotemporal contextual method

GIScience & Remote Sensing (2021)

Wenqiang Xi Shihong Du Shouhang Du Xiuyuan Zhang Haiyan Gu

Land covers provide essential information for understanding and detecting ecosystem, resources, and environmental dynamics. However, they are generally mapped at coarser temporal scales to study the inter-annual changes, while scant attention has been paid to map intra-annual land cover dynamics at finer temporal scales. Moreover, existing studies are still limited in intra-annual land cover mapping with dense satellite image time series (SITS). Accordingly, this study proposed a novel approach to accurately classify dense SITS for mapping intra-annual land cover dynamics. First, dense SITS is segmented at multiple spatiotemporal scales to generate optimal spatiotemporal cubes (ST-cubes), which are chosen as classification units. Second, the ST-cubes based on spectral, textural, spatial, and temporal features are integratively defined and employed in SITS classification. Third, the spatiotemporal context is modeled by a spatiotemporally extended conditional random field model that measures both spatiotemporal features and semantic correlation between geographic objects. Finally, the proposed method is applied to map the intra-annual land cover dynamics. Comparative experiments of SITS classification are carried out between our method and three existing competitors in a suburban area in Beijing, China, with a dense Sentinel-2 SITS. Moreover, based on the classification results, we analyzed the quantitative intra-annual dynamics of land cover. The result shows that our approach achieves significant improvements in classification accuracy over existing methods, indicating the effectiveness and superiority of the proposed method in mapping intra-annual land cover dynamics with dense SITS.

Land Cover

10.1080/15481603.2021.1973216

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Citations (6)

Functional Classification of Urban Parks Based on Urban Functional Zone and Crowd-Sourced Geographical Data

ISPRS International Journal of Geo-Information (2021)

Su Cao Shihong Du Shuwen Yang Shouhang Du

Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures.

Point of interest

Similarity (geometry)

Volunteered Geographic Information

Urban ecosystem

10.3390/ijgi10120824

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Citations (14)

Unrevealing the coupling coordination degree between atmospheric CO2 concentration and human activities from geospatial and temporal perspectives

The Science of The Total Environment (2024)

Yurong Zheng Jianfei Wu Shouhang Du Wenbin Sun Liming He

10.1016/j.scitotenv.2024.173691

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Mapping urban functional zones with remote sensing and geospatial big data: a systematic review

GIScience & Remote Sensing (2024)

Shouhang Du Xiuyuan Zhang Yichen Lei Xin Huang Wei Tu

Geomatics

10.1080/15481603.2024.2404900

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Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion

International Journal of Digital Earth (2024)

Su Cao Xuesheng Zhao Shouhang Du

Urban green space (UGS) is important to the urban ecological environment. It has physical characteristics and social function characteristics and plays an important role in urban climate change, sustainable development goals (SDG) and residents’ health. However, existing researches mostly focus on the extraction of UGS physical features, neglecting the importance of UGS social functions, resulting in the unresolved problem of multi-type and fine-grained functional mapping of UGS. Therefore, based on natural language processing (NLP) and multi-source data fusion, this paper proposes a multi-type and fine-grained UGS function mapping method. First, the social functional standards of UGS have been re-established, with a total of 19 categories. Second, the semantic information in the POI data name text is extracted using the deep learning model, and the reclassification of the UGS type of POI data is realized. Then, combined with multi-source data, 18 types of UGS are extracted. Finally, combining multi-source data to extract urban road green spaces (GS), a fine-grained UGS functional map of Shanghai is created. The results show that the overall accuracy rate of the method is 93.6%, and the Kappa coefficient is 0.93, which proves that the method has good performance in large-scale spatial UGS classification.

Sensor Fusion

Data source

10.1080/17538947.2024.2308723

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Citations (4)