A complex junction recognition method based on GoogLeNet model

Li, Chengming; Zhang, Honggang; Wu, Pengda; Yin, Yong; Liu, Sichao

doi:10.1111/tgis.12681

Cited by 21 publications

(7 citation statements)

References 32 publications

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“…As typical supervised machine learning methods, support vector machine (SVM) [19] and random forest (RF) [20] were selected due to their stable performance in previous studies [43,44]. In addition, GoogLeNet [45] was selected as the baseline deep convolutional network method due to its light size and its proven effectiveness in previous studies [46][47][48]. This section provides descriptions of the algorithms used in the land cover classification experiments.…”

Section: Urban Land Cover Classificationmentioning

confidence: 99%

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

2021

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Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

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Section: Urban Land Cover Classificationmentioning

confidence: 99%

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

2021

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“…The topology is then refreshed, and the isolated and dangling arcs [ 36 , 37 ] that are separated by distances smaller than length threshold value are eliminated, as shown in Figure 6 . In an actual computation, the threshold is automatically determined based on statistical principles, that is, the difference between the average value of the length of all arcs and the standard deviation of the length of all arcs.…”

Section: Methodsmentioning

confidence: 99%

An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics

Wang

Liu

Zhu

et al. 2021

Sensors

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Feature matching plays a crucial role in the process of 3D reconstruction based on the structure from motion (SfM) technique. For a large collection of oblique images, feature matching is one of the most time-consuming steps, and the matching result directly affects the accuracy of subsequent tasks. Therefore, how to extract the reasonable feature points robustly and efficiently to improve the matching speed and quality has received extensive attention from scholars worldwide. Most studies perform quantitative feature point selection based on image Difference-of-Gaussian (DoG) pyramids in practice. However, the stability and spatial distribution of feature points are not considered enough, resulting in selected feature points that may not adequately reflect the scene structures and cannot guarantee the matching rate and the aerial triangulation accuracy. To address these issues, an improved method for stable feature point selection in SfM considering image semantic and structural characteristics is proposed. First, the visible-band difference vegetation index is used to identify the vegetation areas from oblique images, and the line feature in the image is extracted by the optimized line segment detector algorithm. Second, the feature point two-tuple classification model is established, in which the vegetation area recognition result is used as the semantic constraint, the line feature extraction result is used as the structural constraint, and the feature points are divided into three types. Finally, a progressive selection algorithm for feature points is proposed, in which feature points in the DoG pyramid are selected by classes and levels until the number of feature points is satisfied. Oblique images of a 40-km2 area in Dongying city, China, were used for validation. The experimental results show that compared to the state-of-the-art method, the method proposed in this paper not only effectively reduces the number of feature points but also better reflects the scene structure. At the same time, the average reprojection error of the aerial triangulation decrease by 20%, the feature point matching rate increase by 3%, the selected feature points are more stable and reasonable.

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“…GoogLeNet Presentation. GoogLeNet [45] is the champion model of ILSVRC in 2014, with a total of 22 layers. This model points out that the model quality can be improved by increasing the depth or width of the model.…”

Section: Methodsmentioning

confidence: 99%

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

Wang

Yue

et al. 2021

International Journal of Aerospace Engineering

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Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.

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A complex junction recognition method based on GoogLeNet model

Cited by 21 publications

References 32 publications

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics

Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

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