A novel FMH model for road extraction from high-resolution remote sensing images in urban areas

Hong, Muzhu; Guo, Junqi; Dai, Yunrong; Yin, Zhaoyang

doi:10.1016/j.procs.2019.01.183

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…There are some research apply LHT to remote sensing image object detection task, Wang, et al [30] used the Hough transform to judge whether an airport exists in an image. Liu, et al [31] presented a dictionary learning method to approximate the generalized Hough transform for road detection; Hong, et al [32] used the local Hough transform to extract the road area, and then applied morphological operations to modify and refine the shapes of roads. Luo, et al [33] proposed an automatic recognition method combining the improved Otsu segmentation algorithm with LHT to detect the Great Wall of the Han Dynasty based on GF-1 panchromatic data.…”

Section: A Linear Hough Transformmentioning

confidence: 99%

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Xu,

Yu,

Ming

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object detection from remote sensing images is a key technology for earth observation applications, which has important scientific research value. Ground objects in remote sensing images appear at arbitrary angles. However, object detection based on horizontal bounding boxes (HBBs) would cause mutual coverage among targets while the ground objects were dense-distributed or with a large aspect ratio. The oriented object detection methods could solve the problem by predicting the rotation angle. But the currently used methods were timeconsuming in labelling and require complex loss functions of networks. Thus, this paper combined HBB and oriented object detection to achieve rectangular object detection. Firstly, object detection network with HBBs is trained and the model predicts results in original remote sensing image. Secondly, the rotation angles are derived from the line detection with linear Hough transform on the cropped region of targets obtained by the first step. Then, the original image is rotated according to the rotation angles and detects object with HBBs again. Finally, new bounding boxes are mapped to the original image to get the detection results with oriented bounding boxes (OBBs).Experiments on public remote sensing datasets demonstrate that the proposed method is highly flexible and can be combined with any HBBs object detection network. The idea of original image rotation does not use the OBB labels to retrain the network, which reduces the workload of object annotations. Additionally, the method can be used to automatically generate OBBs to label rectangular objects since the public datasets commonly annotated by HBBs.

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Section: A Linear Hough Transformmentioning

confidence: 99%

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Xu,

Yu,

Ming

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In 2019, Hong M, Guo J, Dai Y and others propose a new FMH model, main road information is extracted from high-resolution remote sensing images in urban areas [29]. Huang F, Yu Y, Feng T et al considered that the potential of multi-source remote sensing data was not fully exploited, Low -level features are not effectively organized, IDE accuracy is low, Poor automation.…”

Section: Current Situation Of Remote Sensing Image Road Extraction Based On Deep Learningmentioning

confidence: 99%

Advances in Remote Sensing Extraction of Urban Roads

Liu

2021

E3S Web Conf.

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As early as 1970s, the United States has begun the research of remote sensing image processing technology. In recent ten years, the research of road remote sensing extraction in China has also advanced by leaps and bounds. High resolution remote sensing images have been widely used in many fields, such as urban development planning, environmental monitoring and evaluation, and public announcement information services. The main application goal of remote sensing image is to extract the information of the object of interest, then identify it and complete the image understanding. Road is the most important and basic transportation mode of human beings, which provides a lot of support for the development of human civilization. road extraction is important for traffic management, including urban planning, road monitoring, GPS navigation, map updating, image registration, etc. extracting roads from high-resolution remote sensing satellite images is not only a challenging research direction, but also of great practical value.

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“…Road information can be effectively extracted based on the unique texture features of the road. For example, Hong et al [9] based on the extracted FMH model, Haverkamp [10] used angle texture features to extract urban roads, and combined angle texture features with Chen [11] to use Ziplock Snake algorithm for road extraction. The road texture extraction method has great advantages in extracting urban roads, but it has the disadvantage of poor adaptability to complex situations.…”

Section: Introductionmentioning

confidence: 99%

Research on Road Extraction From High-Resolution Remote Sensing Images Based on Improved UNet++

Li,

Tan,

Xiao

et al. 2024

IEEE Access

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To address the challenges of road extraction in high-resolution remote sensing images, this paper presents an enhanced UNet++ road extraction method that incorporates CBAM. The original UNet++ network is referenced, and the loss function is improved by introducing a new joint loss function. The enhanced UNet++ network utilizes an attention mechanism to enhance the network's ability to identify road features, thereby improving the accuracy of road extraction. Additionally, a new joint loss function is employed to enhance the network's stability and further improve its road extraction capability. Experimental validation is performed on the Massachusetts roads dataset and DeepGlobal road dataset. The experimental results demonstrate that this method outperforms U-Net, SegNet, and UNet++ networks in terms of IoU, Recall, OA, and Kappa. Specifically, on the Massachusetts road dataset, the OA and Kappa values are 94.92% and 0.9202, respectively. On the DeepGlobal road dataset, the OA and Kappa values for this algorithm are 98.12% and 0.9515, respectively. The ablation experiment confirms the effectiveness of the proposed enhancements. In conclusion, this paper presents a method that effectively extracts roads from high-resolution remote sensing images, exhibits a certain level of generalization ability, and can provide valuable support for road protection and planning.

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A novel FMH model for road extraction from high-resolution remote sensing images in urban areas

Cited by 14 publications

References 2 publications

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Advances in Remote Sensing Extraction of Urban Roads

Research on Road Extraction From High-Resolution Remote Sensing Images Based on Improved UNet++

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