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Article ; Online: Method of Land Cover Web Information Discovery

HOU Dongyang

Acta Geodaetica et Cartographica Sinica, Vol 46, Iss 1, Pp 133-

2017 Volume 133

Keywords	Mathematical geography. Cartography ; GA1-1776
Language	Chinese
Publishing date	2017-01-01T00:00:00Z
Publisher	Surveying and Mapping Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A novel multi-attention fusion network with dilated convolution and label smoothing for remote sensing image retrieval

Wang, Siyuan / Hou, Dongyang / Xing, Huaqiao

International Journal of Remote Sensing. 2022 Feb. 16, v. 43, no. 4 p.1306-1322

2022

Abstract: Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus on discriminative features of important objects, resulting in unsatisfactory ... ...

Abstract	Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus on discriminative features of important objects, resulting in unsatisfactory retrieval performance with complex backgrounds and small objects. We therefore propose a multi-attention fusion network with dilated convolution and label smoothing for CBRSIR. First, a dilated convolutional layer is used to replace the fifth convolutional layer in the network to obtain a large receptive field. Then, a contextual transformer attention (CoT) and an efficient channel attention (ECA) are fused together for spatial-wise and channel-wise discriminative features, respectively. The multi-attention module is embedded between the newly added dilated convolutional layer and the followed average pooling layer. Besides, in order to enhance the differences between the discriminative features of those correct and incorrect classes, label smoothing is used to replace the cross-entropy loss function. Some ablation experiments are conducted on six benchmark datasets. Compared to the baseline AlextNet with the above different modules, the mAP values of the proposed network improve by 14.84% to 24.21%. The results indicate that our network can significantly improve the retrieval performance. In addition, we have also conducted some experiments for network migration and comparison with some recent methods (e.g. a triplet deep metric learning and deep feature learning with latent relationship embedding network). Experimental results illustrate that our network can be effectively migrated to other similar CNN models and can achieve state-of-the-art or competitive results.
Keywords	artificial intelligence ; data collection ; fields ; image analysis ; journals ; neural networks ; remote sensing ; remote sensing image retrieval ; contextual transformer attention ; convolutional neural network ; dilated convolution ; label smoothing
Language	English
Dates of publication	2022-0216
Size	p. 1306-1322.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	1497529-4
ISSN	1366-5901 ; 0143-1161
ISSN (online)	1366-5901
ISSN	0143-1161
DOI	10.1080/01431161.2022.2035465
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A method for measuring geometric information content of area cartographic objects based on discrepancy degree of shape points

Kang Qiankun / Zhou Xiaoguang / Hou Dongyang / Ali Nawaz / Luo Silong / Zhao Shaoxuan

Geocarto International, Vol 38, Iss

2023 Volume 1

Abstract: AbstractIn order to improve the comparability between the geometric information content of vector area objects, this article proposes a method for measuring the geometric information content of area objects based on discrepancy degree of shape points. ... ...

Abstract	AbstractIn order to improve the comparability between the geometric information content of vector area objects, this article proposes a method for measuring the geometric information content of area objects based on discrepancy degree of shape points. First, the method selects circles with unique geometric feature as the reference shape for extracting geometric features, and the geometric in-formation carried by each shape point of area objects is represented by the discrepancy degree between the area object and the reference circle at the point position. Second, the proposed method measures the geometric information content of area objects from both local and global perspectives. To avoid the subjectivity of assigning feature weights based on empirical experience, the article uses the relationships between the radii of three reference circles (MIC: Maximum Inscribed Circle, EAC: Equal-area circle, and MCC: Minimum Circumscribed Circle) as adaptive weight parameters for local and global structural geometric information. The amount of geometric information at each shape point is obtained by weighted summation, and the total geometric information content of an area object is the sum of the amount of geometric information of all shape points. To verify the effectiveness and rationality of the proposed method, this article designs a noise simulation dataset for simply building area objects and an empirical ranking dataset for evaluating the measurement performance of the proposed method. The experimental results show that the proposed method achieves a Kendall rank correlation coefficient of 0.88 on the empirical ranking dataset, which is higher than that of the nine existing representative methods. The proposed method is more consistent with human cognition and is highly correlated with the amount and intensity of noise information. Moreover, the proposed method achieves the comparability of geometric information content of area objects and the adaptive determination of geometric feature weights. The proposed ...
Keywords	Information amount ; reference circle ; entropy ; vector data ; area objects ; shape point ; Physical geography ; GB3-5030
Subject code	004
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China.

Zhu, Linye / Xing, Huaqiao / Hou, Dongyang

Scientific reports

2022 Volume 12, Issue 1, Page(s) 8021

Abstract: Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing ... ...

Abstract	Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing the objective of carbon neutrality. Therefore, this paper used GlobeLand30 land cover maps, annual average normalised difference vegetation index (NDVI) data, annual average net ecosystem productivity (NEP) data and statistical yearbook data from 2000 to 2020 to explore the relationship between land cover change and carbon emissions. Specifically, it included land cover change, carbon storage changes influenced by land cover change, spatial and temporal analysis of carbon sources and sinks, land use intensity change and anthropogenic carbon emissions. The results of the study show that the main land cover changes in Shandong province during 2000-2020 was cultivated land conversion to artificial surfaces. Among them, the area of cultivated land converted to artificial surfaces from 2000 to 2010 was 4930.62 km
MeSH term(s)	Carbon/analysis ; China ; Ecosystem ; Human Activities ; Humans ; Industry
Chemical Substances	Carbon (7440-44-0)
Language	English
Publishing date	2022-05-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-12080-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A coastal wetlands mapping approach of Yellow River Delta with a hierarchical classification and optimal feature selection framework

Xing, Huaqiao / Niu, Jingge / Feng, Yongyu / Hou, Dongyang / Wang, Yan / Wang, Zhiqiang

Catena. 2023 Apr., v. 223 p.106897-

2023

Abstract: Wetlands play an important role in ecological health and sustainable development, their spatial distribution and explicit thematic information are crucial for developing management and conservation measures. The Yellow River Delta is an important coastal ...

Abstract	Wetlands play an important role in ecological health and sustainable development, their spatial distribution and explicit thematic information are crucial for developing management and conservation measures. The Yellow River Delta is an important coastal wetland reserve in China, its wetland types are complex and diverse, natural and artificial wetlands are easily confused, making refined classification more difficult. To address this challenge, we proposed a new wetland mapping approach by combing hierarchical classification framework (HCF) and optimal feature selection. First, inheritance-based multiscale segmentation was carried out to obtain object-oriented images, and decision tree classification was used for preliminarily identify wetland and non-wetland. Second, recursive feature elimination and cross-validation (RFECV) was used to select optimal features, which was then utilized for wetland refinement extraction by using random forest (RF) algorithm. The experiments were performed based on Sentinel-1, Sentinel-2 and NASADEM datasets. The results show that effective wetland classification features can be selected by using RFECV. The feature scores are as follows, red edge index > spectral features > vegetation/water body index > backscatter coefficient > topographic features > texture features > location feature > urban index > geometric feature. The overall accuracy and Kappa coefficient of the method in this paper are 92.36 % and 0.915, which are 14.62 % and 6.68 % higher than using only HCF or only RFECV. Compared with the GlobeLand30 and CAS_Wetlands datasets, the refinement of wetland mapping in this paper is higher. This study provides a new idea in methodological selection for wetland information extraction, and the resulting coastal wetland map can be used for sustainable management, ecological assessment and conservation of the Yellow River Delta.
Keywords	algorithms ; catenas ; data collection ; decision support systems ; environmental health ; geometry ; river deltas ; surface water ; sustainable development ; texture ; topography ; vegetation ; wetlands ; China ; Yellow River ; HCF ; RFECV ; Wetland ; Yellow River Delta
Language	English
Dates of publication	2023-04
Publishing place	Elsevier B.V.
Document type	Article ; Online
ZDB-ID	519608-5
ISSN	1872-6887 ; 0008-7769 ; 0341-8162
ISSN (online)	1872-6887 ; 0008-7769
ISSN	0341-8162
DOI	10.1016/j.catena.2022.106897
Database	NAL-Catalogue (AGRICOLA)

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Article: Change Detection Using a Texture Feature Space Outlier Index from Mono-Temporal Remote Sensing Images and Vector Data

Wei, Dongsheng / Hou, Dongyang / Zhou, Xiaoguang / Chen, Jun

Remote Sensing. 2021 Sept. 26, v. 13, no. 19

2021

Abstract: Multi-temporal remote sensing images are the primary sources for change detection. However, it is difficult to obtain comparable multi-temporal images at the same season and time of day with the same sensor. Considering texture homogeneity among objects ... ...

Abstract	Multi-temporal remote sensing images are the primary sources for change detection. However, it is difficult to obtain comparable multi-temporal images at the same season and time of day with the same sensor. Considering texture homogeneity among objects belonging to the same category, this paper presents a new change detection approach using a texture feature space outlier index from mono-temporal remote sensing images and vector data. In the proposed approach, a texture feature contribution index (TFCI) is defined based on information gain to select the optimal texture features, and a feature space outlier index (FSOI) based on local reachability density is presented to automatically identify outlier samples and changed objects. Our approach includes three steps: (1) the sampling method is designed considering spatial distribution and topographic properties of image objects extracted by segmenting the recent image with existing vector map. (2) Samples with changed categories are refined by an iteration procedure of texture feature selection and outlier sample elimination; and (3) the changed image objects are identified and classified using the refined samples to calculate the FSOI values of the image objects. Three experiments in the two study areas were conducted to validate its performance. Overall accuracies of 95.94%, 96.36%, and 96.28% were achieved, respectively, while the omission and commission errors for every category were all very low. Four widely used methods with two-temporal images were selected for comparison, and the accuracy of the proposed method is higher than theirs. This indicates that our approach is effective and feasible.
Keywords	texture ; topography ; vector data
Language	English
Dates of publication	2021-0926
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13193857
Database	NAL-Catalogue (AGRICOLA)

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Article: Integrating change magnitude maps of spectrally enhanced multi-features for land cover change detection

Xing, Huaqiao / Zhu, Linye / Hou, Dongyang / Zhang, Tao

International journal of remote sensing. 2021 June 03, v. 42, no. 11

2021

Abstract: Constructing a change magnitude map (CMM) is a key component of binary change detection. Recently, integrating multiple features to obtain a comprehensive CMM has become a popular research topic. However, the current integration approaches mainly utilize ...

Abstract	Constructing a change magnitude map (CMM) is a key component of binary change detection. Recently, integrating multiple features to obtain a comprehensive CMM has become a popular research topic. However, the current integration approaches mainly utilize simple spectral CMMs that are derived based on a single spectral change index (e.g. image difference, Euclidean distance, and change vector analysis), which is not sufficient for addressing complex land cover changes. In this study, we propose a spectrally enhanced multi-feature fusion (SeMF) method with CMM integration for effective change detection. Seven commonly used spectral change indices are analysed from the aspects of the spectral value and spectral shape; two of these indices are selected to construct the optimal spectral-based CMM, which is more efficient, robust and stable than the single spectral change indices. The rotation-invariant local binary patterns (RiLBP) and Canny methods are further used for CMM generation via the textural and shape features, respectively. These three types of CMMs are adaptively assigned weights by using an information entropy-based fusion strategy and ultimately integrated into a comprehensive CMM. Two groups of experiments with Landsat 8 Operational Land Imager (OLI) and Gaofen (GF)-1 images are designed to verify the effectiveness of the SeMF method. The experimental results indicate that the SeMF method is superior to both spectral feature-based and multi-feature-based change detection methods.
Keywords	Landsat ; design ; image analysis ; journals ; land cover ; remote sensing ; texture ; weight
Language	English
Dates of publication	2021-0603
Size	p. 4284-4308.
Publishing place	Taylor & Francis
Document type	Article
ZDB-ID	1497529-4
ISSN	1366-5901 ; 0143-1161
ISSN (online)	1366-5901
ISSN	0143-1161
DOI	10.1080/01431161.2021.1892860
Database	NAL-Catalogue (AGRICOLA)

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Article: Two novel benchmark datasets from ArcGIS and bing world imagery for remote sensing image retrieval

Hou, Dongyang / Miao, Zelang / Xing, Huaqiao / Wu, Hao

International journal of remote sensing. 2021 Jan. 02, v. 42, no. 1

2021

Abstract: Benchmark datasets are essential to develop and evaluate remote sensing image retrieval (RSIR) approaches. However, there are no two datasets with different remote sensing image sources, an approximate equal number of images, the same classification ... ...

Abstract	Benchmark datasets are essential to develop and evaluate remote sensing image retrieval (RSIR) approaches. However, there are no two datasets with different remote sensing image sources, an approximate equal number of images, the same classification system, and image size in the existing public benchmark datasets. This may affect the evaluation of the universality and robustness of RSIR approaches for the same category in different datasets, and even hinder the development of new cross-source RSIR approaches that require remote sensing images from different sources. We therefore present two new large-scale datasets from ArcGIS and Bing World Imagery, respectively. Similar to the PatternNet dataset from Google Earth Imagery, both two new collected datasets contain 38 classes using the same classification system and each class has at least 1,500 images. We conduct experiments using five handcrafted low/mid-level feature methods and six deep learning high-level feature methods on the two datasets. Results show that our datasets are effective for evaluating different RSIR approaches and the results can be served as the baseline for future research. We also perform the comparison and the cross analysis compared with other large-scale datasets. Results indicate that our datasets are more inclusive, richer variations and better intra-class diversity. Besides, other experimental results show that our new datasets and the VGoogle (extracted by volunteers from Google imagery) dataset can be merged into one dataset for larger-scale remote sensing image retrieval.
Keywords	Internet ; crossing ; data collection ; exhibitions ; extracts ; image analysis ; journals ; remote sensing ; volunteerism ; volunteers
Language	English
Dates of publication	2021-0102
Size	p. 240-258.
Publishing place	Taylor & Francis
Document type	Article
ZDB-ID	1497529-4
ISSN	1366-5901 ; 0143-1161
ISSN (online)	1366-5901
ISSN	0143-1161
DOI	10.1080/01431161.2020.1804090
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A novel change detection method using remotely sensed image time series value and shape based dynamic time warping

Xing, Huaqiao / Zhu, Linye / Chen, Bingyao / Zhang, Liguo / Hou, Dongyang / Fang, Wenbo

Geocarto International. 2022 Dec. 13, v. 37, no. 25 p.9607-9624

2022

Abstract: Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time ... ...

Abstract	Satellite image time series change detection methods provide comprehensive understanding of land cover changes. Traditional bi-temporal change detection methods in satellite image time series require consistent time series lengths and use only time series value or shape to calculate change magnitude maps, which may not fully utilize land cover change information. To address this challenge, we propose a change detection method using remotely sensed image time series value and shape based dynamic time warping (TSVS). Change magnitude maps were obtained from the time series trajectories of NDVI and MNDWI using time series value-based dynamic time warping method and time series shape-based dynamic time warping method. Change detection results were derived by clustering the polar coordinate space of time series value and shape using Gaussian mixture model method. Experiments using Landsat images show that the TSVS method improves about 2.75–5.10% compared to the CVA_GMM method, reducing the generation of false alarms.
Keywords	Landsat ; land cover ; remote sensing ; time series analysis ; Change detection ; time series ; dynamic time warping ; Gaussian mixture model
Language	English
Dates of publication	2022-1213
Size	p. 9607-9624.
Publishing place	Taylor & Francis
Document type	Article ; Online
ISSN	1752-0762
DOI	10.1080/10106049.2021.2022013
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine

Xing, Huaqiao / Chen, Bingyao / Feng, Yongyu / Ni, Yuanlong / Hou, Dongyang / Wang, Xue / Kong, Yawei

Geocarto International. 2022 Dec. 13, v. 37, no. 26 p.13291-13310

2022

Abstract: Accurate distribution of irrigated, rainfed and paddy croplands is essential for food production and agricultural management. High spatial resolution land cover datasets rarely have detailed information about irrigated or rainfed croplands, while ... ...

Abstract	Accurate distribution of irrigated, rainfed and paddy croplands is essential for food production and agricultural management. High spatial resolution land cover datasets rarely have detailed information about irrigated or rainfed croplands, while cropland datasets labelled with crop watering methods face challenges with coarse temporal and spatial resolution. This study proposed a semi-automatic detailed croplands mapping framework by integrating pixel-based classification and image segmentation. First, high-quality Sentinel-2 images were selected and mosaicked to time-series image dataset using Google Earth Engine platform. Second, pixel-based random forest classification and the Simple Non-Iterative Clustering image segmentation were integrated by optimal rules for generating 10-m resolution Detailed Croplands Map (DCM-2020). The DCM-2020 was assessed by using validation samples, and the overall accuracy of the map was 88.6% with kappa coefficient of 0.83. Compared with the existing cropland datasets, DCM-2020 shows much finer details, and croplands area is closer to official statistics.
Keywords	Internet ; agricultural management ; cropland ; data collection ; food production ; image analysis ; irrigation ; land cover ; paddies ; statistics ; time series analysis ; Irrigated mapping ; detailed croplands ; Sentinel-2 ; time series ; random forest classification ; SNIC image segmentation ; Google Earth Engine
Language	English
Dates of publication	2022-1213
Size	p. 13291-13310.
Publishing place	Taylor & Francis
Document type	Article ; Online
ISSN	1752-0762
DOI	10.1080/10106049.2022.2076923
Database	NAL-Catalogue (AGRICOLA)

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