Hyperspectral Imagery for Environmental Urban Planning

Weber, Christian; Aguejdad, Rahim; Briottet, Xavier; Avala, J.; Fabre, Sophie; Demuynck, Jean; Zenou, Emmanuel; Deville, Yannick; Karoui, Moussa Sofiane; Benhalouche, Fatima Zohra; Gadal, Sébastien; Ourghemmi, W.; Mallet, Clément; Bris, Arnaud Le; Chehata, Nesrine

doi:10.1109/igarss.2018.8519085

Cited by 41 publications

(12 citation statements)

References 22 publications

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“…High-spatial-resolution images produce detailed land-cover and land-use information, but the spectral similarity of different objects and shadows of tall buildings or large trees limit the impervious surface extraction (Guo et al 2014). Hyper-spectral images solve the problem of spectral similarity of different objects and spectral heterogeneity of the same land class, but small map coverage and high price obstruct the application of hyper-spectral images (Weber et al 2018). SAR image extracts land information free from cloud, and SAR image is helpful for extracting impervious surfaces under the large tree crowns (Guo et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Sun

Zhao

et al. 2018

J Indian Soc Remote Sens

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The urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization and its environmental impacts. Adopting deep learning technologies, this study proposes an approach of three-dimensional convolutional neural networks (3D CNNs) to extract impervious surfaces from the WorldView-2 and airborne LiDAR datasets. The influences of different 3D CNN parameters on impervious surface extraction are evaluated. In an effort to reduce the limitations from single sensor data, this study also explores the synergistic use of multi-source remote sensing datasets for delineating urban impervious surfaces. Results indicate that our proposed 3D CNN approach has a great potential and better performance on impervious surface extraction, with an overall accuracy higher than 93.00% and the overall kappa value above 0.89. Compared with the commonly applied pixel-based support vector machine classifier, our proposed 3D CNN approach takes advantage not only of the pixel-level spatial and spectral information, but also of texture and feature maps through multi-scale convolutional processes, which enhance the extraction of impervious surfaces. While image analysis is facing large challenges in a rapidly developing big data era, our proposed 3D CNNs will become an effective approach for improved urban impervious surface extraction.

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Section: Introductionmentioning

confidence: 99%

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Sun

Zhao

et al. 2018

J Indian Soc Remote Sens

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“…Recently, hyperspectral remote sensing has been broadly and successfully applied in urban planning [1], precision agriculture [2], environmental monitoring [3] and other fields with the constantly increased spectral resolution of sensors. Hyperspectral remote sensing combines spectral features with spatial images that can accurately identify and detect ground objects, which provides strong technical support for ground feature extraction [4].…”

Section: Introductionmentioning

confidence: 99%

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

et al. 2022

Symmetry

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Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

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“…A hyperspectral image usually provides a good overview of macroscopic surface features. The spectral information contained in the image provides the possibility to distinguish land covers, which also makes hyperspectral images widely used in various fields.This has spawned various practical applications, such as agricultural industry [1], urban planning [2], change detection [3], Lesion detection [4] and mineral detection [5], etc.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification

She¹,

Wang²

2022

Preprint

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Hyperspectral images (HSI) not only have a broad macroscopic field of view but also contain rich spectral information, and the types of surface objects can be identified through spectral information, which is one of the main applications in hyperspectral image related research.In recent years, more and more deep learning methods have been proposed, among which convolutional neural networks (CNN) are the most influential. However, CNN-based methods are difficult to capture long-range dependencies, and also require a large amount of labeled data for model training.Besides, most of the self-supervised training methods in the field of HSI classification are based on the reconstruction of input samples, and it is difficult to achieve effective use of unlabeled samples. To address the shortcomings of CNN networks, we propose a noval multi-scale convolutional embedding module for HSI to realize effective extraction of spatial-spectral information, which can be better combined with Transformer network.In order to make more efficient use of unlabeled data, we propose a new self-supervised pretask. Similar to Mask autoencoder, but our pre-training method only masks the corresponding token of the central pixel in the encoder, and inputs the remaining token into the decoder to reconstruct the spectral information of the central pixel.Such a pretask can better model the relationship between the central feature and the domain feature, and obtain more stable training results.

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Hyperspectral Imagery for Environmental Urban Planning

Cited by 41 publications

References 22 publications

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification

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