C-UNet: Complement UNet for Remote Sensing Road Extraction

Hou, Yuewu; Liu, Zhaoying; Zhang, Ting; Li, Yujian

doi:10.3390/s21062153

Cited by 56 publications

(27 citation statements)

References 39 publications

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“…The model is based on the fully convolutional architecture proposed by Long et al [17]. The UNet architecture has demonstrated excellent results in image recognition in various areas, including the study of medical images [18], the search for roads in aerial photography [19], the detection of clouds and shadows cast by clouds [20], and the detection of defects in textile products [21].…”

Section: Methods For Detecting Defective Zonesmentioning

confidence: 99%

Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

Konovalenko

Maruschak

Kozbur

et al. 2021

Metals

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Features of the defect class “scratches, attritions, lines”, their geometric structure, and their causes are analyzed. An approach is developed that defines subclasses within this class of technological defects based on additional analysis of morphological features. The analysis of the reasons for these subclasses allows additional information to be obtained about the rolling process, identifying additional signs of defects, regulating the rolling conditions of steel strips more accurately, and diagnosing the equipment condition.

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Section: Methods For Detecting Defective Zonesmentioning

confidence: 99%

Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

Konovalenko

Maruschak

Kozbur

et al. 2021

Metals

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“…Inspired by ResNet [29], Zhang et al [30] introduced the deep residual UNet and fewer parameters were used to approach better result by skipping different level residual units. A complement model was proposed in [31]. It fuse the extracted results of different modules to improve the accuracy of model prediction.…”

Section: Related Workmentioning

confidence: 99%

FMWDCT: Foreground Mixup Into Weighted Dual-Network Cross Training for Semisupervised Remote Sensing Road Extraction

You

Wang

Chen

et al. 2022

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

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With the development of deep learning, the application of automatic road extraction has achieved great success. However, the main challenge is how to make full use of a large number of unlabeled images to improve segmentation models and how alleviate sample imbalance in road extraction tasks. In this paper, we propose a novel semi-supervised remote sensing road extraction approach is refined as Foreground Mixup into Weighted Dual-network Cross Training (FMWDCT), which combines labeled images with unlabeled images to extract road from remote sensing images. FMWDCT is composed of Dualnetwork Cross Training (DCT) and Foreground Pasting (FP). DCT is a new semi-supervised training method and FP is an effective data perturbation method for road extraction. We firstly paste the foreground pixels obtained from labeled images into unlabeled images to produce mixed input images. The mixed pseudo labels are then generated by a combination of highconfidence predictions from the augmented network and labeled masks. Finally, the mixed pseudo labels are used to guide another adversarial basic network for cross training, and this basic network is used to smoothly update the augmented network that corresponds to it. The proposed FMWDCT effectively solve the overfitting problem and imbalance problem of positive and negative sample in the case of a few labeled training samples. We demonstrate the effectiveness of our method on three road extraction datasets, and achieve better performance with few labeled data. Extensive experiments show that the proposed semi-supervised method can learn latent information from the unlabeled data to improve performance.

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“…However, there is still room for improvement for CNN applications using large-scale remote sensing datasets, such as the Landsat archives [9]. Despite certain promising research on remote sensing using CNNs [31][32][33], including some denser deep learning models for interesting classification tasks [34,35], the possibility of employing very large-scale Landsat images, even on a global scale, has yet to be fully explored.…”

Section: Introductionmentioning

confidence: 99%

AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data

et al. 2022

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Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.

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C-UNet: Complement UNet for Remote Sensing Road Extraction

Cited by 56 publications

References 39 publications

Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

FMWDCT: Foreground Mixup Into Weighted Dual-Network Cross Training for Semisupervised Remote Sensing Road Extraction

AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data

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