Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

Zhang, Bo; Zhang, Meng; Kang, Jian; Hong, Danfeng; Xu, Jian; Zhu, Xiaoxiang

doi:10.3390/rs11060646

Cited by 25 publications

(19 citation statements)

References 49 publications

(55 reference statements)

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“…In addition to the aerosol vertical distribution, due to the hygroscopic growth of aerosols [26], relative humidity (RH) has to be taken into account in order to estimate accurately the ground PM 10 concentrations from satellite observations data [27]. The relationship between concentration of PM 10 and RH could be expressed as Equation (1) as demonstrated by Yu, et al 6 [28] and Zhang, et al [29]:…”

Section: Pollutant Concentration Estimationmentioning

confidence: 99%

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Wang

et al. 2020

Remote Sensing

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Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM 10 , PM 2.5 , O 3 , NO 2 , SO 2 and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25 • × 0.25 • , show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields.

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Section: Pollutant Concentration Estimationmentioning

confidence: 99%

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Wang

et al. 2020

Remote Sensing

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“…The spatial resolutions of Bands 8 and 9 are 15 m, while the others are 30 m. Compared with the sensors on Landsat 7 satellite, the wavelength of Band 5 is adjusted to 0.845–0.885 µm, thus eliminating the influence of water vapour absorption at 0.825 µm; the panchromatic wave of Band 8 is narrow, so that vegetation and non-vegetation areas can be distinguished better. 20,21…”

Section: Data Collectionmentioning

confidence: 99%

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

Zhang

Hong

2019

Indoor and Built Environment

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Land surface temperature (LST) is an important parameter related to the environmental assessment concerning the surface energy, water balance, greenhouse effect, etc. at local and global scales. With the rapid development of the remote sensing technology, various methodologies have been developed to retrieve LST from space-based remote sensing images. Due to the ill-posed problem, the LST retrieval is still a challenge. In this research, a so-called multiple band reflectance (MBR)-LST model has been proposed based on the back-propagation neural (BPN) network, which can be employed to retrieve the LSTs from Landsat 8 Operational Land Imager (OLI)/TIRS images as well as produce continuous spatial LST distributions with a spatial resolution of 30 m. Experiments conducted in two randomly selected areas in mainland China proved that the proposed MBR-LST model has yielded a much better performance than the traditional radiative transfer equation (RTE) method with respect to both the accuracy and stability for the LST retrievals. Moreover, another significant advantage of the proposed MBR-LST is the generic nature – once trained by the sample data in the whole region of mainland China, the proposed MBR-LST model can be utilized for the accurate LST-retrievals in any area of mainland China.

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“…Currently operational radar (e.g., Sentinel-1) and optical broadband (multispectral) satellites (e.g., Sentinel-2 and Landsat-8) enable the synthetic aperture radar (SAR) ( Kang et al, 2020 ) and multispectral image (MSI) ( Zhang et al, 2019a ) openly available on a global scale. Therefore, there has been a growing interest in understanding our environment through remote sensing (RS) images, which is of great benefit to many potential applications such as image classification ( Tuia et al, 2015 , Han et al, 2018 , Srivastava et al, 2019 , Cao et al, 2020a ), object and change detection ( Zhang et al, 2018b , Zhang et al, 2019b , Wu et al, 2019 , Wu et al, 2020 ), mineral exploration ( Gao et al, 2017a , Hong and Zhu, 2018 , Hong et al, 2019b , Yao et al, 2019 ), multi-modality data analysis ( Hong et al, 2019d , Hong et al, 2020a , Hu et al, 2019a , Yang et al, 2019 ), to name a few.…”

Section: Introductionmentioning

confidence: 99%

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

Hong

Yokoya

Xia

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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191

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This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods.

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Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

Cited by 25 publications

References 49 publications

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

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