Scene parsing for very high resolution remote sensing images using on attention-residual block-embedded adversarial networks

Yan, Ke; Wang, Hui; Bu, Shuhui; Yang, Le; Li, Jing

doi:10.1080/2150704x.2021.1910362

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…In [35], K. Yan et al proposed Attention-Residual Block Embedded Adversarial Networks (AREANs). Attention blocks were added to the decoder path to enhance the feature representation and capture contextual information.…”

Section: Review Of Road Feature Extractionmentioning

confidence: 99%

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

Sharma,

kumar,

Gupta

2023

Preprint

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Automatic extraction of roads based on data-driven methods is essential for various aspects such as route optimization, traffic management, GPS navigation, disaster management, defense, and security intelligence, etc. Due to the occlusion of trees, vehicles, buildings, etc., it is challenging. A systematic review is proposed in this study to overcome the challenges. More than 214 articles from 2018–2022 are collected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines from different databases (i.e., Scopus, Web of Science (WoS), IEEE Explorer), and 44 are reviewed. The article selection process is based on keywords like "Deep Learning (DL)," "Road Extraction," "High- Resolution Remote Sensing Images (HRRSI)," etc. The different datasets used by the researchers are also discussed in this study, along with the type of sensors and satellites used to collect HRRSI images. This study aims to provide a proposed solution to the investigations retrieved from the previous research work. After analysis, it is concluded that the factors retrieved from this rigorous analysis can be considered to propose a novel model that can resolve the issue of accurate extraction of roads.

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Section: Review Of Road Feature Extractionmentioning

confidence: 99%

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

Sharma,

kumar,

Gupta

2023

Preprint

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“…erefore, in the original detection operator, we also need to carry out gray conversion. Gray conversion will cause the loss of image information and the waste of color details [7,8].…”

Section: Obstacle Avoidance Path Planningmentioning

confidence: 99%

Obstacle Avoidance Path Planning Algorithm of an Embedded Robot Based on Machine Vision

Shou

2022

Mobile Information Systems

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Due to the lack of accurate modeling information in environment modeling, the traditional path planning algorithm for robot obstacle avoidance is of low accuracy. Therefore, this paper designs an obstacle avoidance path planning algorithm for embedded robot based on machine vision. First, the method of target edge detection is optimized in this paper. The edge detection results are obtained by color space transformation, and the complete target is obtained by edge fusion combined with surrounding pixel attributes. Then, the distance of the obstacle is measured by binocular depth ranging, and the longitudinal positioning of the robot is obtained, and the position of the obstacle is further obtained. Finally, a fuzzy control method for obstacle avoidance path planning is designed to obtain a complete planning scheme. The performance test results of the obstacle avoidance path algorithm show that the obstacle avoidance path planning scheme obtained by the algorithm designed in this paper has better performance in different obstacle avoidance test environments and can successfully avoid obstacles when the robot runs at high speed.

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“…To improve the information extraction of global context, Jie Jiang et al [2] proposed a novel global-guided selective context network (GSCNet) to select contextual information adaptively. Multi-scale feature fusion and enhancement network (MFFENet) [3], semantic consistency module [4], and attention residual block-embedded adversarial networks (AREANs) [5] can fuse global semantic information at multiple scales. For information extraction of local information features, Shiyu Liu et al [6] highlighted the strong correlation between depth and semantic information by introducing a built-in deep semantic coupling coding module that adaptively fuses RGB and depth features.…”

Section: Introductionmentioning

confidence: 99%

Global enhancement network underwater archaeology scene parsing method

Pan,

Jia,

Cai

2023

Robotica

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Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

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Scene parsing for very high resolution remote sensing images using on attention-residual block-embedded adversarial networks

Cited by 5 publications

References 13 publications

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

Remote Sensing Images based Road Network Extraction using Deep Learning: A Systematic Review

Obstacle Avoidance Path Planning Algorithm of an Embedded Robot Based on Machine Vision

Global enhancement network underwater archaeology scene parsing method

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