A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

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Pattern change and dynamic evolution of urban green space based on multi-temporal remote sensing images: a case study of Xuzhou City

10.1117/12.760788 ◽

2007 ◽

Author(s):

Huapeng Zhang ◽

Peijun Du ◽

Chen Pan ◽

Zuoxia Yin

Keyword(s):

Remote Sensing ◽

Green Space ◽

Urban Green Space ◽

Dynamic Evolution ◽

Remote Sensing Images ◽

Urban Green ◽

Pattern Change ◽

Xuzhou City ◽

Multi Temporal

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Extraction of architectural information from urban green space baced on high resolution remote sensing image

2009 Joint Urban Remote Sensing Event ◽

10.1109/urs.2009.5137689 ◽

2009 ◽

Author(s):

Shuang Xu ◽

Jie Wang ◽

Zihan Hong ◽

Pengfeng Xiao

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Green Space ◽

Remote Sensing Image ◽

Urban Green Space ◽

Urban Green

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Concatenated Residual Attention UNet for Semantic Segmentation of Urban Green Space

Forests ◽

10.3390/f12111441 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1441

Author(s):

Guoqiang Men ◽

Guojin He ◽

Guizhou Wang

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Green Space ◽

Spatial Information ◽

Urban Green Space ◽

Remote Sensing Images ◽

Large Area ◽

Urban Green ◽

Urban Green Spaces ◽

Residual Structure

Urban green space is generally considered a significant component of the urban ecological environment system, which serves to improve the quality of the urban environment and provides various guarantees for the sustainable development of the city. Remote sensing provides an effective method for real-time mapping and monitoring of urban green space changes in a large area. However, with the continuous improvement of the spatial resolution of remote sensing images, traditional classification methods cannot accurately obtain the spectral and spatial information of urban green spaces. Due to complex urban background and numerous shadows, there are mixed classifications for the extraction of cultivated land, grassland and other ground features, implying that limitations exist in traditional methods. At present, deep learning methods have shown great potential to tackle this challenge. In this research, we proposed a novel model called Concatenated Residual Attention UNet (CRAUNet), which combines the residual structure and channel attention mechanism, and applied it to the data source composed of GaoFen-1 remote sensing images in the Shenzhen City. Firstly, the improved residual structure is used to make it retain more feature information of the original image during the feature extraction process, then the Convolutional Block Channel Attention (CBCA) module is applied to enhance the extraction of deep convolution features by strengthening the effective green space features and suppressing invalid features through the interdependence of modeling channels.-Finally, the high-resolution feature map is restored through upsampling operation by the decoder. The experimental results show that compared with other methods, CRAUNet achieves the best performance. Especially, our method is less susceptible to the noise and preserves more complete segmented edge details. The pixel accuracy (PA) and mean intersection over union (MIoU) of our approach have reached 97.34% and 94.77%, which shows great applicability in regional large-scale mapping.

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Extraction of the urban green space based on the high resolution remote sensing image

10.1117/12.832786 ◽

2009 ◽

Author(s):

Miaomiao Cheng ◽

Hong Jiang ◽

Jian Chen ◽

Zheng Guo ◽

Zishan Jiang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Green Space ◽

Remote Sensing Image ◽

Urban Green Space ◽

Urban Green

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Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Remote Sensing ◽

10.3390/rs12182985 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2985 ◽

Cited By ~ 1

Author(s):

Yeneng Lin ◽

Dongyun Xu ◽

Nan Wang ◽

Zhou Shi ◽

Qiuxiao Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Network Models ◽

Road Extraction ◽

Remote Sensing Images ◽

Proposed Model ◽

Wide Range ◽

Network Modules ◽

Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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