Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

Zheng, Zhijia; Du, Shihong; Taubenböck, Hannes; Zhang, Xiuyuan

doi:10.1016/j.rse.2022.112913

Cited by 28 publications

(27 citation statements)

References 182 publications

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“…It has a huge number of applications in many areas like land use and cover concerns [47] disaster management, atmosphere changes, and many more [48]. ML is the subdivision of artificial intelligence (AI) [49]. ML basically designs an algorithm to be able to learn from the data to predict something out of it.…”

Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Section: Machine Learningmentioning

confidence: 99%

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

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“…Then, combined with selected papers, extensive and in-depth reading was carried out to introduce UAV platforms and sensors, and the application scenarios of UAV remote sensing in grassland ecosystem monitoring were summarized and analyzed from five aspects. Combined with relevant research [8,15,18,19], a literature measurement method was adopted to select research articles from the core database of ISI Web of Science, a commonly used and reliable database for scientific publications, and we conducted two rounds of progressive retrieval. In the first round, the keyword was "UAV remote sensing", while in the second round, the keyword was "grassland", with a retrieval period from 2000 to 2020.…”

Section: Uav Remote Sensing Technology and Grassland Ecosystem 21 Dev...mentioning

confidence: 99%

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

Lyu

Dang

et al. 2022

Remote Sensing

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In recent years, the application of unmanned aerial vehicle (UAV) remote sensing in grassland ecosystem monitoring has increased, and the application directions have diversified. However, there have been few research reviews specifically for grassland ecosystems at present. Therefore, it is necessary to systematically and comprehensively summarize the application of UAV remote sensing in grassland ecosystem monitoring. In this paper, we first analyzed the application trend of UAV remote sensing in grassland ecosystem monitoring and introduced common UAV platforms and remote sensing sensors. Then, the application scenarios of UAV remote sensing in grassland ecosystem monitoring were reviewed from five aspects: grassland vegetation monitoring, grassland animal surveys, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring. Finally, the current limitations and future development directions were summarized. The results will be helpful to improve the understanding of the application scenarios of UAV remote sensing in grassland ecosystem monitoring and to provide a scientific reference for ecological remote sensing research.

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“…(c) Multi-scale region attention module The attention mechanism can effectively use global statistics to enhance the salient features of the target to be segmented in remote sensing images, and meanwhile suppress non-salient features such as noise and background on the premise of not reducing the spatial resolution of the image [32,33]. Therefore, numerous remote sensing image semantic segmentation methods have been proposed based on the pixellevel spatial attention mechanisms.…”

Section: A Encoder-decoder Segmentation Network Based On Rea-netmentioning

confidence: 99%

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

Deng

Meng

et al. 2022

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

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Aimed at the challenge of low accuracy of building segmentation caused by poor continuity of remote-sensing-image regions and blurred boundaries, a remote sensing building semantics segmentation algorithm based on multi-scale regional consistent attention supervision is proposed. Firstly, based on the Unet encoder-decoder architecture, the proposed algorithm constructs the region attention network (ReA-Net), which employs a multi-scale receptive field-guidance model to simultaneously focus on regional features and edge details of remote sensing image objects. Secondly, the self-attention mechanism is employed to establish the correlation representation of regional-level features of remote sensing images, and multi-scale regional attention features of remote sensing images are obtained through weighted regional-level correlation mapping. Finally, to address the lack of spatial correlation constraints on the prediction of remote sensing images segmentation, a loss function with multi-scale neighborhood consistency supervision is suggested to constrain the consistency of pixel label assignment related to a local region. Experimental results on WHU Building Dataset showed that Intersection over Union (IOU) reached 91.6%, precision reached 95.61%, recall reached 95.68% recall and F1-score reached 95.64%; On the Massachusetts building dataset, IOU reached 74.77% and precision reached 83.93%, recall reached 87.53% and F1-score reached 85.69%. Therefore, the proposed algorithm not only has a good segmentation effect but also has a strong robustness for remote sensing building image segmentation.

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Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

Cited by 28 publications

References 182 publications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Remote Sensing Image Classification: A Comprehensive Review and Applications

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

ReA-Net: A Multiscale Region Attention Network With Neighborhood Consistency Supervision for Building Extraction From Remote Sensing Image

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