The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in parti...The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aquifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments conducted an elevated permeability zone.展开更多
Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clus...Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.展开更多
文摘The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aquifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments conducted an elevated permeability zone.
基金funded by Scientific and Technological Innovation Team of Universities in Henan Province,grant number 22IRTSTHN008Innovative Research Team(in Philosophy and Social Science)in University of Henan Province grant number 2022-CXTD-02the National Natural Science Foundation of China,grant number 41371524.
文摘Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.
