Water Quality Analysis of Remote Sensing Images Based on Inversion Model

Wang, Jinzhe; Zhang, Junping; Li, Tong; Wang, Xiao

doi:10.1109/igarss.2018.8519442

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…(i) The proposed method adapts the concepts of SIGO, GWBP and VGG16 for feature extraction in new way to solve the complex problem of multiple-class classification of clean and polluted water images. (ii) The way [18] Wu et al [25] Zhao et al .11 The limitations of the proposed method (2020) 2:2068 | https://doi.org/10.1007/s42452-020-03882-w better than existing methods for both two and multiple class classification. (v) It is also noted from experimental results that the proposed method is robust to rotation and scaling and to some extent to noise and blur at different levels.…”

Section: Discussionmentioning

confidence: 99%

“…The methods discussed in this work are limited to the quality of the water images but not for classifying different water type images. Wang et al [25] proposed water quality analysis for remote sensing images based on an inversion model. The method proposes spectral reflectance and water quality parameters for analyzing the quality of the images.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…(23):where means step size.When the process reaches n , we calculate the residual as defined in Eq. (25).…”

Section: Gradient Boosting Decision Tree (Gbdt) For Water Image Classmentioning

confidence: 99%

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Deep invariant texture features for water image classification

Xue

Shivakumara

et al. 2020

SN Appl. Sci.

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Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

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Deep invariant texture features for water image classification

Xue

Shivakumara

et al. 2020

SN Appl. Sci.

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“…In 2013, the Landsat 8 satellite was launched with two sensors OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), and 11 satellite frequency bands with characteristics that allow the identification of different contents in the target regions. Trescott & Park (2013) use the bands 1, 2 and 3 for analyzing eutrophication and Wang et al (2018) for analyzing oxygen and dissolved permanganate concentrations. In addition to bands 2 and 3, Pisani et al (2016) include band 4 to determine the sedimentation present in two Brazilian rivers.…”

Section: Introductionmentioning

confidence: 99%

Analysis of water quality at hydrographic basin scale using satellite images, co-occurrence matrices and Bayes classifier

Silva

Costa

et al. 2021

Water Supply

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Given the increased risks of water scarcity and the presence of polluting agents in water resources, this paper aims at the development and presentation of a computational tool capable of assessing water quality based on digital processing techniques applied to satellite images. Initially, a database was created for Brazilian regions, consisting of hydrographic basins satellite images associated with the Water Quality Index (WQI), according to the criteria established by the National Water Agency (ANA). Hitherto, the database consists of 85 images, 61 are used in the training stage and 24 in the testing stage. In both stages, the images are subjected to thresholding using the Otsu's Method, binarization, linear expansion on saturation, application of a Laplacian filter, extraction of characteristics by using co-occurrence matrices and classification by the Bayes Discriminant. Such techniques were also implemented on a computational platform in MATLAB® environment, responsible for the interface between the system and users. The proposed system presented an approximate 70% success rate regarding the classification of WQIs, which can be improved as more information is made available to improve the databases.

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“…In environmental monitoring, we can analyze the ecological status of water quality, hydrological distribution, chlorophyll distribution, suspended solids concentration, etc [5] . B. K. Purandara, B. S. Jamadar et al analysed the water quality of The Vembanad Lake in South India using remote sensing [6] .Wang, J. , Zhanget al used Landsat 8 OLI data to invert the potassium permanganate index of Songhua River in Harbin [7] . In this paper, principal component analysis was used to screen out the main evaluation factors in the water quality factor, and regression analysis was used to screen out the corresponding sensitive bands and establish a regression prediction model.…”

Section: Introductionmentioning

confidence: 99%

Quantitative remote sensing monitoring of water quality in Bohai Bay based on Landsat multispectral data

Wei

2020

E3S Web Conf.

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In this paper, through the principal component analysis of water quality survey data of Bohai Bay in 2006, 2009 and 2013, the main pollutant was selected, and the quasi-simultaneous Landsat multispectral remote sensing data are regressed to establish the quantitative inversion model of the sensitive band and the main pollutants in seawater. The accuracy of the model is determined to meet the requirements of quantitative inversion of water quality remote sensing through the significance test method of accuracy assessment, providing a basis for future multispectral remote sensing monitoring of water quality indicators. assessment, providing a basis for future multispectral remote sensing monitoring of water quality indicators.

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Water Quality Analysis of Remote Sensing Images Based on Inversion Model

Cited by 7 publications

References 4 publications

Deep invariant texture features for water image classification

Deep invariant texture features for water image classification

Analysis of water quality at hydrographic basin scale using satellite images, co-occurrence matrices and Bayes classifier

Quantitative remote sensing monitoring of water quality in Bohai Bay based on Landsat multispectral data

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