The Experts below are selected from a list of 40929 Experts worldwide ranked by ideXlab platform
Guoping Qiu - One of the best experts on this subject based on the ideXlab platform.
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Integrating Aerial and Street View Images for Urban Land Use Classification
Remote Sensing, 2018Co-Authors: Rui Cao, Jiasong Zhu, Jinzhou Cao, Bozhi Liu, Qian Zhang, Guoping QiuAbstract:Urban Land Use is key to rational urban planning and management. Traditional Land Use Classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban Land Use at pixel level using high-resolution aerial images and ground-level street view images. We Use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fUsed together through a deep neural network for classifying Land Use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high Classification accuracy, the ground-level street view images contain Useful information for urban Land Use Classification, and fusing street image features with aerial images can improve Classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide Useful auxiliary information to aerial images to boost performances.
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CBMI - Urban Land Use Classification Based on Aerial and Ground Images
2018 International Conference on Content-Based Multimedia Indexing (CBMI), 2018Co-Authors: Rui Cao, Guoping QiuAbstract:Urban Land Use is key to rational urban planning and management. Traditional Land Use Classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, we explore to utilise deep neural network based approaches to label urban Land Use at pixel level using high-resolution aerial images and ground-level street images. We Use a deep neural network to extract semantic features from sparsely distributed street images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fUsed together through a deep neural network for classifying Land Use categories. We test our methods on a large publicly available aerial and street images dataset of New York City, and the results show that using aerial images alone can achieve relatively high Classification accuracy and the ground-level street views contain Useful information for urban Land Use Classification. Fusing street image features with aerial images can improve Classification accuracy to some extent but the improvement is somewhat limited.
Peng Gong - One of the best experts on this subject based on the ideXlab platform.
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information fusion for rural Land Use Classification with high resolution satellite imagery
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: Wanxiao Sun, V Heidt, Peng GongAbstract:We propose an information fusion method for the extraction of Land-Use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery. It integrates spectral, spatial and structural information existing in the image. A thematic map was first produced with a maximum-likelihood Classification (MLC) applied to the multispectral imagery. Probabilistic relaxation (PR) was then performed on the thematic map to refine the Classification with neighborhood information. Furthermore, we incorporated edges extracted from the higher resolution panchromatic imagery in the Classification. An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was Used to improve image Classification. The procedure proved to be more effective in Land-Use Classification than conventional methods based only on multispectral data. The improved Land-Use map is characterized with sharp interregional boundaries, reduced number of mixed pixels and more homogeneous regions. The overall kappa statistics increased considerably from 0.52 before the fusion to 0.75 after.
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a comparison of spatial feature extraction algorithms for Land Use Classification with spot hrv data
Remote Sensing of Environment, 1992Co-Authors: Peng Gong, Danielle J Marceau, Philip J HowarthAbstract:Abstract A large number of spatial feature extraction methods were developed during the past 20 years. The effectiveness of each method has been assessed in different studies using different data. However, there have been few application-oriented studies made to evaluate the relative powers of these methods in a particular environment. In this study, three spatial feature extraction methods have been compared in the Land-Use Classification of the SPOT HRV multispectral data at the rural-urban fringe of Metropolitan Toronto. The first two methods are the well-known gray level co-occurrence matrix (GLCM) and the simple statistical transformation (SST). The third method is the texture spectrum (TS), which was developed recently. Twenty-seven spatial features were derived from the SPOT HRV Band 3 image using these methods. Each of these features or a combination of two of these features were Used in combination with the three spectral images in the Classification of 10 Land-Use classes. Results indicated that some spatial features derived using the GLCM and the SST methods can largely improve the Classification accuracies obtained by the Use of the spectral images only. In addition, average transformed divergence was found to be ineffective in selecting optimal spatial features for Land-Use Classification.
Rui Cao - One of the best experts on this subject based on the ideXlab platform.
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Integrating Aerial and Street View Images for Urban Land Use Classification
Remote Sensing, 2018Co-Authors: Rui Cao, Jiasong Zhu, Jinzhou Cao, Bozhi Liu, Qian Zhang, Guoping QiuAbstract:Urban Land Use is key to rational urban planning and management. Traditional Land Use Classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban Land Use at pixel level using high-resolution aerial images and ground-level street view images. We Use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fUsed together through a deep neural network for classifying Land Use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high Classification accuracy, the ground-level street view images contain Useful information for urban Land Use Classification, and fusing street image features with aerial images can improve Classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide Useful auxiliary information to aerial images to boost performances.
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CBMI - Urban Land Use Classification Based on Aerial and Ground Images
2018 International Conference on Content-Based Multimedia Indexing (CBMI), 2018Co-Authors: Rui Cao, Guoping QiuAbstract:Urban Land Use is key to rational urban planning and management. Traditional Land Use Classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, we explore to utilise deep neural network based approaches to label urban Land Use at pixel level using high-resolution aerial images and ground-level street images. We Use a deep neural network to extract semantic features from sparsely distributed street images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fUsed together through a deep neural network for classifying Land Use categories. We test our methods on a large publicly available aerial and street images dataset of New York City, and the results show that using aerial images alone can achieve relatively high Classification accuracy and the ground-level street views contain Useful information for urban Land Use Classification. Fusing street image features with aerial images can improve Classification accuracy to some extent but the improvement is somewhat limited.
Peter M Atkinson - One of the best experts on this subject based on the ideXlab platform.
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an object based convolutional neural network ocnn for urban Land Use Classification
Remote Sensing of Environment, 2018Co-Authors: Ce Zhang, Isabel Sargent, Xin Pan, Andy Gardiner, Jonathon S Hare, Peter M AtkinsonAbstract:Urban Land Use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban Land Use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban Land Use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the Classification of urban Land Use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban Land Use Classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are Used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific Classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent Classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban Land Use Classification from VFSR images.
Jiangfeng She - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Land Use Classification based on convolutional neural network
Journal of Applied Remote Sensing, 2020Co-Authors: Liuming Wang, Junxiao Wang, Jiangfeng SheAbstract:Deep learning methods have been developed and widely Used in Land Use Classification with remote sensing images. In addition, due to the different datasets Used in different studies, there is a lack of direct comparison between different deep learning model applications in Land Use Classification. The open source dataset DeepSat was Used to build and test a convolutional neural network (CNN) model. The convolution kernels in the model were extracted to further study the specific features learned by the deep learning model. In addition, different CNN-based models were compared to explore the impacts of model structures on model accuracy. The major conclusions from the research are: (1) CNN model is effective in Land Use Classification, with an accuracy of 0.9998 and 0.9991 for the SAT-4 and SAT-6 data, respectively; (2) CNN does have a “learning” ability that can extract the most critical and effective information from training datasets; and (3) for remote sensing Land Use Classification, increasing the number of convolution kernels is better than adding more convolutional layers. Max pooling is better than average pooling. In addition, a localized response normalized layer can also improve model accuracy.
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an integrated framework combining multiple human activity features for Land Use Classification
ISPRS international journal of geo-information, 2019Co-Authors: Shuhua Zhang, Liwei Zhang, Jiangfeng SheAbstract:Urban Land Use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate Classification of Land Use extremely challenging. Human activity features extracted from big data have been Used for Land Use Classification, and fusing different features can help improve the Classification. In this paper, we propose a framework to integrate multiple human activity features for Land Use Classification. Features were fUsed by constructing a membership matrix reflecting the fuzzy relationship between features and Land Use types using the fuzzy c-means (FCM) clustering method. The Classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fUse different features and increase the accuracy of Land Use Classification. The Classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fUsed for Land Use Classification.
