Road extraction base on Zernike algorithm on SAR image

Mu, Huilin; Zhang, Yun; Li, Hongbo; Guo, Yiran; Zhuang, Yuan

doi:10.1109/igarss.2016.7729323

Cited by 16 publications

(12 citation statements)

References 2 publications

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“…Shi et al [10] and Miao et al [11] adopted the adaptive classification method to analyze the multi-spectral features and combined road geometric features to extract non-occluded roads. Que et al [12], Shanmugam et al [13], and Mu et al [14] compared each pixel grayscale feature in remote sensing images with one or more thresholds for road detection and declared that the difference and critical points help determine the optimal threshold. Tan and Zeng realized road extraction and detection by using the edge detection algorithm of the Sobel operator and Canny operator [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Road Extraction Method of a High-Resolution Remote Sensing Image Based on Multi-Feature Fusion and the Attention Mechanism

Jiang¹,

Li²,

Yang³

et al. 2022

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Road extraction from high-resolution remote sensing images has a lot of practical value and significance and has been a research hotspot. Considering that methods based on deep learning and the attention mechanism have achieved good performance in road detection, this paper proposes a deep residual network and an attention mechanism based on the fusion of multiple road features. The encoder-decoder structure of the U-net network with strong multitasking generality is adopted as the basic network. It integrates the spatial multi-scale and multi-channel features of the road to enhance the robustness of feature extraction. Meanwhile, the decoder design based on the attention mechanism further improves the recognition accuracy and effectively curbs the increase in computing cost and time cost. A loss function based on the gradient coordination mechanism is introduced to address the imbalance of road sample data. Finally, experimental verification is carried out on two public road datasets and both qualitative and quantitative comparisons are conducted. Results show that the proposed method is satisfactory and outperforms other methods.

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Section: Introductionmentioning

confidence: 99%

A Road Extraction Method of a High-Resolution Remote Sensing Image Based on Multi-Feature Fusion and the Attention Mechanism

Jiang¹,

Li²,

Yang³

et al. 2022

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“…The pixel-based methods can make use of the difference of spectral characteristics to distinguish the road from other objects at a pixel level. Mu et al [9] proposed the Otsu threshold method to obtain binary images which contain only road and non-road pixels. Coulibaly et al [10] adopted the spectral angle to extract road network by selecting road pixels according to the spectral angle threshold.…”

Section: Introductionmentioning

confidence: 99%

DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery

Wan

Xie

et al. 2021

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

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Recent advances in deep-learning methods have shown extraordinary performance in road extraction from high resolution satellite imagery. However, most existing deep-learning network models yield discontinuous and incomplete results because of shadows and occlusions. To address this problem, a Dual-Attention Road extraction Network (DA-RoadNet) with a certain semantic reasoning ability is proposed. Firstly, DA-RoadNet is designed based on a shallow encoder-to-decoder network with densely connected blocks, which can minimize the loss of road structure information caused by multiple downsampling operations. Moreover, by constructing a novel attention mechanism module, the proposed network is able to explore and integrate the invisible correlations among road features with their global dependency in spatial and channel dimension respectively. Finally, considering that the proportion of road samples is small in the satellite imagery, a hybrid loss function is appended to handle class imbalance, which enables the network model to train stablely and avoid local optimum. The validation experiments using two open road datasets demonstrate that the proposed DA-RoadNet can effectively solve discontinuous problems and preserve integrity of the extracted roads, thus resulting in a higher accuracy of road extraction compared with other developed stateof-the-arts. The considerable performance on the two challenging benchmarks also proves the powerful generation ability of our method.

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“…Researchers usually use spectral information to segment the images first, and then extract roads according to shape features [11], texture features [12], spectral characteristics [13] or pixel footprints [14,15]. Alternatively, threshold segmentation based on the gray value of images is the other common pixel-based method [16][17][18]. Its algorithm is not complicated and is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

et al. 2021

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Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35%-12.8% and 2.41-9.8, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

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Road extraction base on Zernike algorithm on SAR image

Cited by 16 publications

References 2 publications

A Road Extraction Method of a High-Resolution Remote Sensing Image Based on Multi-Feature Fusion and the Attention Mechanism

A Road Extraction Method of a High-Resolution Remote Sensing Image Based on Multi-Feature Fusion and the Attention Mechanism

DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery

Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

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