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Dr. Shihong Du
Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China

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0 GIS
0 Image Analysis
0 Machine Learning
0 Remote Sensing
0 Spatial Analysis

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Journal article
Published: 29 January 2021 in Remote Sensing
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Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

ACS Style

Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing 2021, 13, 477 .

AMA Style

Xiaoting Li, Tengyun Hu, Peng Gong, Shihong Du, Bin Chen, Xuecao Li, Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing. 2021; 13 (3):477.

Chicago/Turabian Style

Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. 2021. "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method." Remote Sensing 13, no. 3: 477.

Journal article
Published: 15 September 2020 in Remote Sensing
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The fast and accurate creation of land use/land cover maps from very-high-resolution (VHR) remote sensing imagery is crucial for urban planning and environmental monitoring. Geographic object-based image analysis methods (GEOBIA) provide an effective solution using image objects instead of individual pixels in VHR remote sensing imagery analysis. Simultaneously, convolutional neural networks (CNN) have been widely used in the image processing field because of their powerful feature extraction capabilities. This study presents a patch-based strategy for integrating deep features into GEOBIA for VHR remote sensing imagery classification. To extract deep features from irregular image objects through CNN, a patch-based approach is proposed for representing image objects and learning patch-based deep features, and a deep features aggregation method is proposed for aggregating patch-based deep features into object-based deep features. Finally, both object and deep features are integrated into a GEOBIA paradigm for classifying image objects. We explored the influences of segmentation scales and patch sizes in our method and explored the effectiveness of deep and object features in classification. Moreover, we performed 5-fold stratified cross validations 50 times to explore the uncertainty of our method. Additionally, we explored the importance of deep feature aggregation, and we evaluated our method by comparing it with three state-of-the-art methods in a Beijing dataset and Zurich dataset. The results indicate that smaller segmentation scales were more conducive to VHR remote sensing imagery classification, and it was not appropriate to select too large or too small patches as the patch size should be determined by imagery and its resolution. Moreover, we found that deep features are more effective than object features, while object features still matter for image classification, and deep feature aggregation is a critical step in our method. Finally, our method can achieve the highest overall accuracies compared with the state-of-the-art methods, and the overall accuracies are 91.21% for the Beijing dataset and 99.05% for the Zurich dataset.

ACS Style

Bo Liu; Shihong Du; Shouji Du; Xiuyuan Zhang. Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach. Remote Sensing 2020, 12, 3007 .

AMA Style

Bo Liu, Shihong Du, Shouji Du, Xiuyuan Zhang. Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach. Remote Sensing. 2020; 12 (18):3007.

Chicago/Turabian Style

Bo Liu; Shihong Du; Shouji Du; Xiuyuan Zhang. 2020. "Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach." Remote Sensing 12, no. 18: 3007.

Journal article
Published: 05 August 2020 in Remote Sensing of Environment
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Imbalance of residential environments in cities (e.g., slums and wealthy districts) causes serious social inequality, negatively impacting livable city development and attracting worldwide attentions. Previous studies on residential environmental quality (REQ) mainly focused on general evaluations at city level, while ignored the spatial heterogeneity of REQ inside cities and failed to survey REQ at local scales. This study recognizes the heterogeneity of REQ strongly related to land-use patterns, and aims to explore how local land-use patterns influence REQ. Firstly, a multimodal semantic segmentation method is presented to classify land uses by using satellite images, building data and points of interests. Secondly, a feature system is defined to characterize land-use patterns, which can be extracted at multiple scales based on land-use classification results. Thirdly, these features are fitted with REQ survey data by random forest regressions, which can predict REQ scores across Beijing and give deep insights into how land-use patterns influence REQ. Experimental results indicate that 1) REQ of Beijing is strongly heterogeneous, and our method can generate a REQ map revealing REQ's imbalance across the city; 2) land-use patterns within 700 m have significant impacts on the local REQ; 3) spatial allocations of land uses are more important than proportions for influencing REQ; and 4) our method visualizes the rules that land-use patterns influence REQ, thus can assist urban land-use planning to balance and improve REQ.

ACS Style

Xiuyuan Zhang; Shihong Du; Shouji Du; Bo Liu. How do land-use patterns influence residential environment quality? A multiscale geographic survey in Beijing. Remote Sensing of Environment 2020, 249, 112014 .

AMA Style

Xiuyuan Zhang, Shihong Du, Shouji Du, Bo Liu. How do land-use patterns influence residential environment quality? A multiscale geographic survey in Beijing. Remote Sensing of Environment. 2020; 249 ():112014.

Chicago/Turabian Style

Xiuyuan Zhang; Shihong Du; Shouji Du; Bo Liu. 2020. "How do land-use patterns influence residential environment quality? A multiscale geographic survey in Beijing." Remote Sensing of Environment 249, no. : 112014.

Journal article
Published: 30 October 2019 in ISPRS International Journal of Geo-Information
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Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range estimator (DFHRE) is proposed in this study, based on the active learning method (ALM). The Euclidean distance is replaced by the cost distance-induced geodesic distance transformation to account for the effects of terrain and obstacles. Three datasets are used to verify the proposed method, and comparisons with the kernel density-based estimator (KDE) and the local convex hulls (LoCoH) estimators and the cross validation test indicate that the proposed estimator outperforms the KDE and the LoCoH estimators.

ACS Style

Jifa Guo; Shihong Du; Zhenxing Ma; Hongyuan Huo; Guangxiong Peng; Guo; Du; Ma; Huo; Peng. A Model for Animal Home Range Estimation Based on the Active Learning Method. ISPRS International Journal of Geo-Information 2019, 8, 490 .

AMA Style

Jifa Guo, Shihong Du, Zhenxing Ma, Hongyuan Huo, Guangxiong Peng, Guo, Du, Ma, Huo, Peng. A Model for Animal Home Range Estimation Based on the Active Learning Method. ISPRS International Journal of Geo-Information. 2019; 8 (11):490.

Chicago/Turabian Style

Jifa Guo; Shihong Du; Zhenxing Ma; Hongyuan Huo; Guangxiong Peng; Guo; Du; Ma; Huo; Peng. 2019. "A Model for Animal Home Range Estimation Based on the Active Learning Method." ISPRS International Journal of Geo-Information 8, no. 11: 490.

Journal article
Published: 14 August 2019 in Remote Sensing
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Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

ACS Style

Shouji Du; Shihong Du; Bo Liu; Xiuyuan Zhang. Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach. Remote Sensing 2019, 11, 1902 .

AMA Style

Shouji Du, Shihong Du, Bo Liu, Xiuyuan Zhang. Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach. Remote Sensing. 2019; 11 (16):1902.

Chicago/Turabian Style

Shouji Du; Shihong Du; Bo Liu; Xiuyuan Zhang. 2019. "Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach." Remote Sensing 11, no. 16: 1902.

Journal article
Published: 01 August 2019 in Remote Sensing
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The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs.

ACS Style

Yuning Feng; Shihong Du; Soe W. Myint; Mi Shu. Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China. Remote Sensing 2019, 11, 1802 .

AMA Style

Yuning Feng, Shihong Du, Soe W. Myint, Mi Shu. Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China. Remote Sensing. 2019; 11 (15):1802.

Chicago/Turabian Style

Yuning Feng; Shihong Du; Soe W. Myint; Mi Shu. 2019. "Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China." Remote Sensing 11, no. 15: 1802.

Journal article
Published: 25 July 2019 in Remote Sensing
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The spectral uncertainty refers to the diversity and variations of spectral characteristics within a single geographic object or across different objects of the same class. Usually, existing methods represent the spectral characteristics as precise single-valued curves. Thus, the spectral variations cannot be modeled, which further restricts the analysis and classification performance of remote sensing images. On the other hand, unsupervised methods have poor performance in classification and modeling uncertainty, while supervised methods need a large number of samples with high quality. Fuzzy semi-supervised clustering (FSSC) methods achieve a high accuracy with limited labelled samples. Thus, currently, FSSC methods attract more and more attention. This paper proposes a novel method to model the spectral uncertainty for very-high-resolution (VHR) images based on interval type-2 fuzzy sets (IT2 FSs), namely the hierarchical semi-supervising and weighted interval type-2 fuzzy c-means for objects (hierarchical SSW-IT2FCM-O) clustering method. In this method, the VHR image is segmented into image objects to reduce spectral uncertainty within objects. Spectral values, spectral indices and textures were weighted for object-based image classification. To further reduce spectral uncertainty across different objects of the same class, the spectral characteristics of land cover types were represented as banded curves with certain widths instead of precise single-valued spectral curves. The experimental results show that the banded spectral curves produced by the hierarchical SSW-IT2FCM-O can effectively model the spectral uncertainty of geographic objects. From the perspective of classification, four typical validity indices along with the confusion matrix and kappa coefficient were used to test the effectiveness of the hierarchical SSW-IT2FCM-O method, and these indices show that the presented method SSW-IT2FCM-O has greater classification accuracy than the existing FSSC methods and, more importantly, it requires smaller training samples than the existing methods.

ACS Style

Jifa Guo; Shihong Du; Hongyuan Huo; Shouji Du; Xiuyuan Zhang. Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering. Remote Sensing 2019, 11, 1750 .

AMA Style

Jifa Guo, Shihong Du, Hongyuan Huo, Shouji Du, Xiuyuan Zhang. Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering. Remote Sensing. 2019; 11 (15):1750.

Chicago/Turabian Style

Jifa Guo; Shihong Du; Hongyuan Huo; Shouji Du; Xiuyuan Zhang. 2019. "Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering." Remote Sensing 11, no. 15: 1750.

Journal article
Published: 24 July 2018 in ISPRS International Journal of Geo-Information
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Modeling qualitative distance words is important for natural language understanding, scene reconstruction and many decision support systems (DSSs) based on a geographic information system (GIS). However, it is difficult to establish the relationship between qualitative distance words and quantitative distance for special applications since the meanings of these words are influenced by both subjective and objective factors. Some existing methods are reviewed, and the Hao–Mendel approach (HMA) is improved to model qualitative distance words for four travel modes by using interval type-2 fuzzy sets (IT2 FSs), aiming at addressing the individual and interpersonal uncertainty among qualitative distance words. The area of the footprint of uncertainty (FOU), fuzziness (entropy), and variance are adopted to measure the uncertainties of qualitative distance words. The experimental results show that the improved HMA algorithm is better than the original HMA algorithm and can be used in spatial information retrieval and GIS-based DSSs.

ACS Style

Jifa Guo; Shihong Du. Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets. ISPRS International Journal of Geo-Information 2018, 7, 291 .

AMA Style

Jifa Guo, Shihong Du. Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets. ISPRS International Journal of Geo-Information. 2018; 7 (8):291.

Chicago/Turabian Style

Jifa Guo; Shihong Du. 2018. "Modeling Words for Qualitative Distance Based on Interval Type-2 Fuzzy Sets." ISPRS International Journal of Geo-Information 7, no. 8: 291.

Journal article
Published: 31 October 2016 in GIScience & Remote Sensing
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Although much efforts have been made to develop automatic methods for building extraction from very high-resolution (VHR) imagery during the past 30 years; the methods with high performance are still unavailable due to the three issues: uncertainty of segmentation scales, selection of effective features, and sample selection. In this study, by introducing GIS data, a parameter mining approach is proposed to (1) mine parameter information for building extraction, and (2) detect changes of buildings between VHR imagery and GIS data. For the first target, the learning mechanism is proposed for identifying optimal segmentation scales, feature subsets, and samples. For the second target, the discovered information (i.e., optimal segmentation scales, feature subsets, and selected samples) is applied to classify the VHR imagery with a multilevel random forest (RF) classifier. The proposed approach is validated on two datasets: Dataset 1 and Dataset 2. The knowledge of building extraction is first learned from Dataset 1 and then used to classify both datasets, and change detection is conducted on Dataset 1. Results of change detection in Dataset 1 indicate that the false alarm ratio and omission error of increased buildings are 20.1% and 8.4%, while the false alarm ratio and omission error of destroyed buildings are 19.1% and 11.3%, respectively. Results of building extraction in Dataset 2 revealed scores of 81.50% and 81.09% at pixel- and object-based evaluation levels. Accordingly, our proposed method is successful in building extraction and change detection.

ACS Style

Zhou Guo; Shihong Du. Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data. GIScience & Remote Sensing 2016, 54, 38 -63.

AMA Style

Zhou Guo, Shihong Du. Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data. GIScience & Remote Sensing. 2016; 54 (1):38-63.

Chicago/Turabian Style

Zhou Guo; Shihong Du. 2016. "Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data." GIScience & Remote Sensing 54, no. 1: 38-63.