Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

2018 ◽  
Vol 214 ◽  
pp. 73-86 ◽  
Author(s):  
Bo Huang ◽  
Bei Zhao ◽  
Yimeng Song
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Use ◽  
Convolutional Neural Network ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Urban Land Use ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Imagery ◽  
Multispectral Remote Sensing

scholarly journals High Spatial Resolution Remote Sensing Scene Classification Method using Transfer Learning and Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Wenmei Li ◽  
Juan Wang ◽  
Ziteng Wang ◽  
Yu Wang ◽  
Yan Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Deep Convolutional Neural Network ◽  
Classification Method ◽  
Scene Classification ◽  
High Quality

Deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high spatial resolution remote sensing (HSRRS) scenes, due to its powerful feature extraction capabilities. It is well-known that huge high quality labeled datasets are required for achieving the better classification performances and preventing over-fitting, during the training DeCNN model process. However, the lack of high quality datasets often limits the applications of DeCNN. In order to solve this problem, in this paper, we propose a HSRRS image scene classification method using transfer learning and DeCNN (TL-DeCNN) model in few shot HSRRS scene samples. Specifically, three typical DeCNNs of VGG19, ResNet50 and InceptionV3, trained on the ImageNet2015, the weights of their convolutional layer for that of the TL-DeCNN are transferred, respectively. Then, TL-DeCNN just needs to fine-tune its classification module on the few shot HSRRS scene samples in a few epochs. Experimental results indicate that our proposed TL-DeCNN method provides absolute dominance results without over-fitting, when compared with the VGG19, ResNet50 and InceptionV3, directly trained on the few shot samples.

scholarly journals High Spatial Resolution Remote Sensing Scene Classification Method using Transfer Learning and Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Wenmei Li ◽  
Juan Wang ◽  
Ziteng Wang ◽  
Yu Wang ◽  
Yan Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Deep Convolutional Neural Network ◽  
Classification Method ◽  
Scene Classification ◽  
High Quality

Deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high spatial resolution remote sensing (HSRRS) scenes, due to its powerful feature extraction capabilities. It is well-known that huge high quality labeled datasets are required for achieving the better classification performances and preventing over-fitting, during the training DeCNN model process. However, the lack of high quality datasets often limits the applications of DeCNN. In order to solve this problem, in this paper, we propose a HSRRS image scene classification method using transfer learning and DeCNN (TL-DeCNN) model in few shot HSRRS scene samples. Specifically, three typical DeCNNs of VGG19, ResNet50 and InceptionV3, trained on the ImageNet2015, the weights of their convolutional layer for that of the TL-DeCNN are transferred, respectively. Then, TL-DeCNN just needs to fine-tune its classification module on the few shot HSRRS scene samples in a few epochs. Experimental results indicate that our proposed TL-DeCNN method provides absolute dominance results without over-fitting, when compared with the VGG19, ResNet50 and InceptionV3, directly trained on the few shot samples.

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

2019 ◽  
Vol 8 (1) ◽  
pp. 28 ◽  
Author(s):  
Quanlong Feng ◽  
Dehai Zhu ◽  
Jianyu Yang ◽  
Baoguo Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Urban Land ◽  
Urban Land Use ◽  
Lidar Data ◽  
Adaptive Fusion

Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on "Squeeze-and-Excitation Networks"). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.

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