Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3+

Fu, Junjie; Yi, Xiaomei; Wang, Guoying; Mo, Lan; Wu, Peng; Kapula, Kasanda Ernest

doi:10.3390/s22197477

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…Currently, the combination of high-resolution remote sensing images and deep learning methods is widely used in semantic segmentation [67] and change detection [68]. High-resolution remote sensing images have many advantages, including providing rich geospatial information, enhancing classification accuracy, alleviating the problem of "the different objects with the same spectrum" and "the same object with different spectrum", and supporting fine-grained classification.…”

Section: Discussionmentioning

confidence: 99%

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

Lin,

Quan,

et al. 2023

Remote Sensing

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Urban vegetation plays a crucial role in the urban ecological system. Efficient and accurate extraction of urban vegetation information has been a pressing task. Although the development of deep learning brings great advantages for vegetation extraction, there are still problems, such as ultra-fine vegetation omissions, heavy computational burden, and unstable model performance. Therefore, a Separable Dense U-Net (SD-UNet) was proposed by introducing dense connections, separable convolutions, batch normalization layers, and Tanh activation function into U-Net. Furthermore, the Fake sample set (NIR-RG), NDVI sample set (NDVI-RG), and True sample set (RGB) were established to train SD-UNet. The obtained models were validated and applied to four scenes (high-density buildings area, cloud and misty conditions area, park, and suburb) and two administrative divisions. The experimental results show that the Fake sample set can effectively improve the model’s vegetation extraction accuracy. The SD-UNet achieves the highest accuracy compared to other methods (U-Net, SegNet, NDVI, RF) on the Fake sample set, whose ACC, IOU, and Recall reached 0.9581, 0.8977, and 0.9577, respectively. It can be concluded that the SD-UNet trained on the Fake sample set not only is beneficial for vegetation extraction but also has better generalization ability and transferability.

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Section: Discussionmentioning

confidence: 99%

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

Lin,

Quan,

et al. 2023

Remote Sensing

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“…The output multi-scale high-level feature map is combined with the low-level feature map using a series of convolution operations and the up-sampling method of bilinear interpolation. Following a bilinear interpolation, the image size is restored to conclude the image segmentation task [39,40].…”

Section: Model Improvement and Trainingmentioning

confidence: 99%

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Wang

Qiu

et al. 2023

Sustainability

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The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone.

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“…In addition to that, Fu et al 24 enhanced the DeepLabV3 + network for high-resolution remote sensing images by incorporating the MobileNetV2 network as the backbone terrain extraction network, introducing attention mechanisms and focus loss balancing. Lv et al 25 addressed the detection challenges in non-uniform remote sensing images by incorporating a multi-scale convolution module and focus dice combination loss function into the U-Net network framework, significantly improving detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Zhang,

Roslan,

Wang

et al. 2023

Sci Rep

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In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent.

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Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3+

Cited by 15 publications

References 29 publications

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

Research on land cover classification of multi-source remote sensing data based on improved U-net network

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