Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

Yu, Long; Wang, Zhiyin; Tian, Shengwei; Ye, Feiyue; Ding, Jianli; Kong, Jun

doi:10.1142/s1469026817500018

Cited by 87 publications

(61 citation statements)

References 8 publications

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“…(4) The global subspaces and the local subspaces are obtained according to Equations (9-13). (5) The statistics and control limits are calculated for monitoring according to Equations (15)(16)(17)(18)(19)(20)(21). (6) Most similar mode information of faulty data is determined according to Equation (14).…”

Section: Online Mode Identification and Monitoringmentioning

confidence: 99%

“…To improve the capability of multimode process monitoring, some methods, such as multiple models methods, Gaussian mixture model, localized Fisher discriminant analysis, etc., have been developed. [15,[20][21][22][23] Multiple models methods were developed by building different monitoring models for different modes, which may be effective for each mode. [18] However, the correlations between different modes were neglected, which may cause a high false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

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Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Zhou

Shi

et al. 2018

Can J Chem Eng

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To handle multimodal uncertainty and dynamics which are common in actual industrial processes, this paper proposes a multimode process monitoring method based on hierarchical mode division and subspace decomposition. First, hierarchical mode division is presented to achieve hierarchical mode information for multimode process modelling, which can improve the adaptability of industrial processes with multimodal uncertainty. Then, hierarchical subspace decomposition is presented to decompose multimode processes into multiple global subspaces and multiple local subspaces for monitoring. Lastly, the most similar mode with various hierarchical modes at the current sampling point is dynamically determined for monitoring, which considers multimodal dynamics. The simulation tests of the penicillin fermentation processes are developed to show feasibility and superiority of the proposed method.

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Section: Online Mode Identification and Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Zhou

Shi

et al. 2018

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…For remote sensing images, the CNN and FCN have been widely applied in scene classifications [35,39], land cover classifications [40,41] and target detections [37] during recent years. Moreover, some studies have reported the application of the CNN or FCN in the surface water mapping community [29,42]. Yu et al presented a novel CNN with a logistic regression to identify water via Landsat [42].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, some studies have reported the application of the CNN or FCN in the surface water mapping community [29,42]. Yu et al presented a novel CNN with a logistic regression to identify water via Landsat [42]. Furthermore, an FCN was proposed to map surface water by training a feature from a different land cover type [29].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Jian

Long

et al. 2018

Remote Sensing

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Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25-100%, and the kappa coefficients of the MLP range from 0.965-1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1-7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

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“…The CNN approach uses convolutional windows and local connections to effectively extract the spatial information. In regard to LULC classification that is based on moderate or coarser resolution images, such as Landsat and MODIS, the CNN approach and texture features may still be advantageous in some special cases, such as cropland classifications or in the extraction of certain LULC types in special regions [21][22][23]. However, for moderate resolution LULC classification with high thematic resolution, very few researches involved neighborhood characteristics using methods like GLCM and CNN.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features

et al. 2018

Remote Sensing

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Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data.

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Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

Cited by 87 publications

References 8 publications

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features

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