Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images

Li, Jing; Liu, Yong; Zhang, Yindan; Zhang, Yang

doi:10.3390/ijgi10050329

Cited by 50 publications

(28 citation statements)

References 52 publications

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“…On the Massachusetts road dataset, C-UNet obtained the mIoU of 63.5% and the mDC of 75.8%. The CADUNET, a composite semantic segmentation network model proposed in Li et al [26]. On the Massachusetts road dataset, this approach found 64.12% IoU.…”

Section: Automatic/deep Learning Methodsmentioning

confidence: 99%

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

Ahmed,

Foysal,

Chaity

et al. 2023

IET Image Processing

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The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet.

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Section: Automatic/deep Learning Methodsmentioning

confidence: 99%

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

Ahmed,

Foysal,

Chaity

et al. 2023

IET Image Processing

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show abstract

“…Since CNN has caused many breakthroughs in computer vision tasks of natural images, it has been widely employed in the work of the semantic segmentation of RSI 35 .Some researchers have used CNN for some specific applications on RSI. These tasks have included but have not been limited to the extraction of multiple classes of geo-objects in the image, as in this paper, but also the extraction of only a single class of geo-objects, such as building extraction 36 , 37 , road extraction 38 – 40 , cloud and snow detection 41 , and urban village mapping 42 . Some models were developed, such as AWNet 43 , HA-MPPNet 44 and HED-UNet 45 .…”

Section: Related Workmentioning

confidence: 99%

A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet

Wang

Shi

et al. 2023

Sci Rep

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Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to the diverse landscapes and different sizes of geo-objects that RSI contains, making semantic segmentation challenging. In this paper, a convolutional network, named Adaptive Feature Fusion UNet (AFF-UNet), is proposed to optimize the semantic segmentation performance. The model has three key aspects: (1) dense skip connections architecture and an adaptive feature fusion module that adaptively weighs different levels of feature maps to achieve adaptive feature fusion, (2) a channel attention convolution block that obtains the relationship between different channels using a tailored configuration, and (3) a spatial attention module that obtains the relationship between different positions. AFF-UNet was evaluated on two public RSI datasets and was quantitatively and qualitatively compared with other models. Results from the Potsdam dataset showed that the proposed model achieved an increase of 1.09% over DeepLabv3 + in terms of the average F1 score and a 0.99% improvement in overall accuracy. The visual qualitative results also demonstrated a reduction in confusion of object classes, better performance in segmenting different sizes of object classes, and better object integrity. Therefore, the proposed AFF-UNet model optimizes the accuracy of RSI semantic segmentation.

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“…The attention mechanism is another popular method for improving regional continuity, Refs. [31,[46][47][48][49][50] introduced spatial and channel attention layers, which effectively improved the segmentation performance, especially for continuity in the road area. Benefiting from the Generative Adversarial Networks (GAN), Refs.…”

Section: Related Workmentioning

confidence: 99%

Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

Liao

Ding

et al. 2021

IJGI

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Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

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Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images

Cited by 50 publications

References 52 publications

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet

Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

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