Research and application of UAV-based hyperspectral remote sensing for smart city construction

Wang, Shunmin; Li, Shelei; Yang, Boxiong; Zhao, Fang; Ali, Faizan; He, Hui

doi:10.1016/j.cogr.2022.12.002

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…The goal is to model the linear relationship between the independent variable 'x' and the dependent variable 'y' to be analyzed [40]. The basic model of MLR is the same as Equation (5).…”

Section: Methodsmentioning

confidence: 99%

“…Hyperspectral remote sensing can be an alternative to existing water-content measurement methods in that it can perform range-type measurements, not existing point measurements. This field acquires correlation information about objects, such as spatial, radiation, and spectral information, and allows the entire observation data, including high-level spectral details, to be easily and quickly obtained [5]. Hyperspectral information is a type of spectral curve that can analyze the spectral characteristics of ground objects during aerial photography.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

Lee,

Park,

Hong

2024

Applied Sciences

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In road construction, the compaction of the subgrade layer, which is one of the earthwork fields, is an essential procedure to support the pavement layer and traffic load. For the quality control of subgrades, water content must be measured. Currently, the measurement of water content is performed at specific locations in a large area of subgrades and has the disadvantage of taking a long time to derive. Because this is difficult to immediately confirm, inefficiencies arise in terms of construction schedule and quality control. Therefore, in this study, a CCM (Color-Coded Map) was proposed through hyperspectral remote sensing using drones. This method is a range-type water-content measurement method that can acquire data in a short time (about 20 min) and can be easily confirmed visually. For this, a predicted equation that can convert hyperspectral information into water content information is developed. Multivariate linear regression, a machine learning technique, was applied to the database (of actual measured water content and hyperspectral information). The predicted and measured water contents showed a coefficient of determination of 0.888, and it was confirmed that CCMs can also be presented in various ways depending on user settings.

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“…The goal is to model the linear relationship between the independent variable 'x' and the dependent variable 'y' to be analyzed [40]. The basic model of MLR is the same as Equation (5).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

Lee,

Park,

Hong

2024

Applied Sciences

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“…Therefore, hyperspectral images can store and acquire comprehensive and in-depth feature information. This characteristic allows hyperspectral images to be effectively applied in various fields, such as resources [2,3], environment [4], urban planning [5], ecology [6], and geology [7,8]. Especially in geology, the identification and classification of surface lithology is fundamental.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network

Zhang,

Liu,

Yang

et al. 2024

Remote Sensing

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Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods.

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“…HSIs contain rich spectral information, which can reflect the unique spectral characteristics of ground objects [3]. HSIs are widely applied in many fields, including geological exploration [4], environmental research [5], agricultural applications [6], and city layout planning [7]. However, because of the limited number of photons in each band, the spatial resolution of HSIs is usually low, which limits their practical application.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution

Yao

Liu

et al. 2023

Remote Sensing

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Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods.

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Research and application of UAV-based hyperspectral remote sensing for smart city construction

Cited by 8 publications

References 9 publications

Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

Hyperspectral Image Classification Based on Double-Branch Multi-Scale Dual-Attention Network

Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution

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