The Experts below are selected from a list of 17868 Experts worldwide ranked by ideXlab platform
Zuoqi Chen - One of the best experts on this subject based on the ideXlab platform.
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Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data
IEEE Geoscience and Remote Sensing Letters, 2018Co-Authors: Bailang Yu, Min Tang, Qiusheng Wu, Chengshu Yang, Shunqiang Deng, Chen Peng, Jianping Wu, Zuoqi ChenAbstract:Accurate information on urban Areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban Areas. Various urban mapping methods have been proposed in the literature to extract urban Built-Up Areas from the Defense Meteorological Satellite Program's Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban Built-Up Area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban Areas derived using each method against the referenced urban Areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban Built-Up Areas and nonurban Areas. The selected methods for urban Built-Up Area extraction were found to perform better on the log-transformed NTL data than the original NTL data.
Shengzhou Xiong - One of the best experts on this subject based on the ideXlab platform.
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Multi-branch convolutional neural network for Built-Up Area extraction from remote sensing image
Neurocomputing, 2019Co-Authors: Shengzhou XiongAbstract:Abstract Built-Up Area is one of the most important objects of remote sensing images analysis, therefore extracting Built-Up Area from remote sensing image automatically has attracted wide attention. It is common to treat Built-Up Area extraction as image segmentation task. However, it's hard to devise a handcrafted feature to describe Built-Up Area since it contains many non-Built-Up elements, such as trees, grasslands, and small ponds. Besides, Built-Up Area corresponds to large size local region without precise boundary in remote sensing image so that the precision of segmentation in pixel level is not reliable. To cope with the problem of Built-Up Area extraction, a segmentation framework based on deep feature learning and graph model is proposed. The segmentation procedure comprises of three steps. Firstly, the image is divided into small patches whose deep features are extracted by the devised lightweight multi-branch convolutional neural network (LMB-CNN). Secondly, a patch-wise graph model is constructed according to the learnt features, and then is optimized to segment Built-Up Area with patch-level precision in full frame of remote sensing image. At last, post-processing step is also adopted to make the segmentation result visually intact. The experiments verify that the proposed method shows excellent performance by achieving high overall accuracy over 98.6% on Gaofen-2 remote sensing image data with size of 10240 × 10240.
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Precise Extraction of Built-Up Area Using Deep Features
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Shengzhou Xiong, Yaming LiAbstract:Built-Up Area is one of the most important objects in remote sensing image analysis, therefore extracting Built-Up Area automatically has attracted wide attention. Deep convolution neural network (CNN) was proposed to improve poor generalization ability of artificial features which had been adopted by traditional automatic extraction methods. In this paper, a more efficient CNN model is proposed to extract the deep features of remote sensing images, and then a graph model based on deep features is constructed to the full image for Built-Up Area extraction. The experiments demonstrate that it has very good performance on the satellite remote sensing image data set.
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IGARSS - Precise Extraction of Built-Up Area Using Deep Features
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Shengzhou Xiong, Yaming LiAbstract:Built-Up Area is one of the most important objects in remote sensing image analysis, therefore extracting Built-Up Area automatically has attracted wide attention. Deep convolution neural network (CNN) was proposed to improve poor generalization ability of artificial features which had been adopted by traditional automatic extraction methods. In this paper, a more efficient CNN model is proposed to extract the deep features of remote sensing images, and then a graph model based on deep features is constructed to the full image for Built-Up Area extraction. The experiments demonstrate that it has very good performance on the satellite remote sensing image data set.
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Automatic Extraction of Built-Up Areas From Panchromatic and Multispectral Remote Sensing Images Using Double-Stream Deep Convolutional Neural Networks
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Shengzhou Xiong, Yansheng LiAbstract:As the central Area of human activities, Built-Up Area has been one of the most important objects that are recognized from a remote sensing image. Built-Up Area in different regions has characteristics as follows: the structure and texture of the Built-Up Area are complex and diverse; the buildings have multitudinous materials; the vegetation distribution and background around the Built-Up Area are changeable. The existing Built-Up Area detection methods still face the challenge to achieve favorable precision and generalization ability. In this paper, a double-stream convolutional neural network (DSCNN) model is proposed to extract the Built-Up Area automatically, which can combine the complementary cues of high-resolution panchromatic and multispectral image. Some postprocessing steps are adopted to make the results more reasonable. We manually annotated a large-scale dataset for training and testing DSCNN. Experiments demonstrate that the proposed method has a higher overall accuracy as well as better generalization ability compared to the state-of-the-art techniques.
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Automatic extraction of Built-Up Area based on deep convolution neural network
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Shengzhou XiongAbstract:Built-Up Area has been one of the most important objects to be extracted in remote sensing images. Several factors such as complex structure, diverse texture and varied background, bring the challenges for the task of Built-Up Area extraction. In this paper, a multiple input structure of deep convolution neural network (CNN) is proposed to extract Built-Up Area automatically, which can fuse the information of panchromatic and multispectral remote sensing image. The image patch based classification results are further refined by postprocessing of segmentation techniques. The experiments demonstrate that the proposed method has better generalization ability compared to the state-of-the-art method, and the overall classification accuracy is above 98%.
Peijun Li - One of the best experts on this subject based on the ideXlab platform.
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Urban Built-Up Area Change Detection Using Multi-Band Temporal Texture and One-Class Random Forest
2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2019Co-Authors: Xiaoxue Feng, Peijun LiAbstract:High spatial resolution multi-temporal data provide more spatial details for mapping and monitoring urban land change from unbuilt to Built-Up Areas. Spatio-temporal contextual information has been recognized as useful features in identification of urban land expansion. This paper adopts a multi-band temporal texture measure with pseudo cross multivariate variogram (PCMV) to quantify local spatiotemporal dependence between bi-temporal multispectral images. The multi-band temporal texture features obtained are combined with bi-temporal multispectral images to detect urban Built-Up Area change using direct multi-temporal image classification. One-class random forest (OCRF), a recently proposed one-class classifier, is used in multi-temporal image classification by sampling one specific class of interest, i.e., Built-Up Area expansion. Experimental results demonstrated that the incorporation of multi-band temporal textures outperformed that using spectral features alone, with increases of 10.55% and 1.74% in overall accuracy, for SPOT-5 and Sentinel-2 data, respectively. Similarly, its overall accuracy is 11.08% and 0.24% higher than incorporating single-band temporal textures. The inclusion of PCMV multi-band temporal texture in direct multi-temporal image classification by OCRF is found effective in urban Built-Up Area change detection.
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An alternative method of urban Built-Up Area extraction using Landsat time series data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Jun Zhang, Peijun Li, Hongwei Zhang, Shu Peng, Ming LiAbstract:Urban Built-Up Area information is pivotal to understand complex drivers and mechanisms in global climate change application. However, Built-Up Area extraction using Landsat time series data is a challenging task due to spatial-temporal expression and modeling of land cover types. To provide insights into the intra-annual dynamics of land use change, focusing on how time series characteristics improves recognition of urban , this paper presents an alternative method to Built-Up Area extraction using intra-annual time series of Landsat images. The central premise of the approach is that time series characteristics is firstly expressed by using spectral data, index and feature. The random forests algorithm is then used in classification process for Built-Up Area extraction. The proposed method is further compared with methods using single temporal Landsat data, using features selected by laplacian score and using different classifiers. Results demonstrate that the proposed method improves the accuracy of urban Area extraction.
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IGARSS - An alternative method of urban Built-Up Area extraction using Landsat time series data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Jun Zhang, Peijun Li, Hongwei Zhang, Shu Peng, Ming LiAbstract:Urban Built-Up Area information is pivotal to understand complex drivers and mechanisms in global climate change application. However, Built-Up Area extraction using Landsat time series data is a challenging task due to spatial-temporal expression and modeling of land cover types. To provide insights into the intra-annual dynamics of land use change, focusing on how time series characteristics improves recognition of urban , this paper presents an alternative method to Built-Up Area extraction using intra-annual time series of Landsat images. The central premise of the approach is that time series characteristics is firstly expressed by using spectral data, index and feature. The random forests algorithm is then used in classification process for Built-Up Area extraction. The proposed method is further compared with methods using single temporal Landsat data, using features selected by laplacian score and using different classifiers. Results demonstrate that the proposed method improves the accuracy of urban Area extraction.
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Urban Built-Up Area extraction using combined spectral information and multivariate texture
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013Co-Authors: Jun Zhang, Peijun Li, Haiqing XuAbstract:Urban Built-Up Area information is required by many applications, such as research of urbanization rate. Urban Built-Up Area extraction using moderate resolution remotely sensed data (e.g. Landsat TM/ETM+) presents numerous challenges, such as very heterogeneous spectral features of urban Areas, spectral confusion between Built-Up class and others. Considering that image texture is one of the important spatial information for identifying urban land cover, a new methodology to address these issues is proposed. This approach involves processes as the following, as a first step, multivariate texture is computed through multivariate variogram. Spectral bands and multivariate texture are then combined in classification process for Built-Up Area extraction. One-Class Support Vector Machine (OCSVM) classifier was used in this process. A comprehensive evaluation is present with Landsat TM data of Beijing, China. Results demonstrate that the proposed method significantly improves the accuracy of urban Area extraction.
Ming Li - One of the best experts on this subject based on the ideXlab platform.
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An alternative method of urban Built-Up Area extraction using Landsat time series data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Jun Zhang, Peijun Li, Hongwei Zhang, Shu Peng, Ming LiAbstract:Urban Built-Up Area information is pivotal to understand complex drivers and mechanisms in global climate change application. However, Built-Up Area extraction using Landsat time series data is a challenging task due to spatial-temporal expression and modeling of land cover types. To provide insights into the intra-annual dynamics of land use change, focusing on how time series characteristics improves recognition of urban , this paper presents an alternative method to Built-Up Area extraction using intra-annual time series of Landsat images. The central premise of the approach is that time series characteristics is firstly expressed by using spectral data, index and feature. The random forests algorithm is then used in classification process for Built-Up Area extraction. The proposed method is further compared with methods using single temporal Landsat data, using features selected by laplacian score and using different classifiers. Results demonstrate that the proposed method improves the accuracy of urban Area extraction.
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IGARSS - An alternative method of urban Built-Up Area extraction using Landsat time series data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Jun Zhang, Peijun Li, Hongwei Zhang, Shu Peng, Ming LiAbstract:Urban Built-Up Area information is pivotal to understand complex drivers and mechanisms in global climate change application. However, Built-Up Area extraction using Landsat time series data is a challenging task due to spatial-temporal expression and modeling of land cover types. To provide insights into the intra-annual dynamics of land use change, focusing on how time series characteristics improves recognition of urban , this paper presents an alternative method to Built-Up Area extraction using intra-annual time series of Landsat images. The central premise of the approach is that time series characteristics is firstly expressed by using spectral data, index and feature. The random forests algorithm is then used in classification process for Built-Up Area extraction. The proposed method is further compared with methods using single temporal Landsat data, using features selected by laplacian score and using different classifiers. Results demonstrate that the proposed method improves the accuracy of urban Area extraction.
Bailang Yu - One of the best experts on this subject based on the ideXlab platform.
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Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data
IEEE Geoscience and Remote Sensing Letters, 2018Co-Authors: Bailang Yu, Min Tang, Qiusheng Wu, Chengshu Yang, Shunqiang Deng, Chen Peng, Jianping Wu, Zuoqi ChenAbstract:Accurate information on urban Areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban Areas. Various urban mapping methods have been proposed in the literature to extract urban Built-Up Areas from the Defense Meteorological Satellite Program's Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban Built-Up Area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban Areas derived using each method against the referenced urban Areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban Built-Up Areas and nonurban Areas. The selected methods for urban Built-Up Area extraction were found to perform better on the log-transformed NTL data than the original NTL data.