Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue i...Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.展开更多
Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful w...Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.展开更多
Water is an essential natural resource without which life wouldn’t exist.The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district,Tamil Nadu,India,using analytic hierarchy...Water is an essential natural resource without which life wouldn’t exist.The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district,Tamil Nadu,India,using analytic hierarchy process(AHP)model.Remote sensing and magnetic parameters have been used to determine the evaluation indicators for groundwater occurrence under the ArcGIS environment.Groundwater occurrence is linked to structural porosity and permeability over the predominantly hard rock terrain,making magnetic data more relevant for locating groundwater potential zones in the research area.NE-SW and NW-SE trending magnetic breaks derived from reduction to pole map are found to be more significant for groundwater exploration.The lineaments rose diagram indicates the general trend of the fracture to be in the NE-SW direction.Assigned normalised criteria weights acquired using the AHP model was used to reclassify the thematic layers.As a result,the taluk’s low,moderate,and high potential zones cover 25.08%,25.68%and 49.24%of the study area,respectively.The high potential zones exhibit characteristics favourable for groundwater infiltration and storage,with factors as gentle slope of<3°,high lineament densities,magnetic breaks,magnetic low zones as indicative of dykes and cracks,lithology as colluvial deposits and land surface with dense vegetation.The depth of the fracture zones was estimated using power spectrum and Euler Deconvolution method.The groundwater potential mapping results were validated using groundwater level data measured from the wells,which indicated that the groundwater potential zoning results are consistent with the data derived from the real world.展开更多
The China Remote Sensing Satellite Ground Station was established in 1986.It currently has three receiving stations in the north,west,and south of China,with the capacity to receive data from 15 international and dome...The China Remote Sensing Satellite Ground Station was established in 1986.It currently has three receiving stations in the north,west,and south of China,with the capacity to receive data from 15 international and domestic Earth observation satellites covering the entire Chinese territory and 70%of Asia.Meanwhile,a systematic,integrated,and standardized spatial information service system has been built.A data-sharing project for medium-resolution Earth observation satellites has been conducted and plays an important role in land,ocean,and atmospheric resource investigation and environmental monitoring.展开更多
针对传统遥感图像数据分析算法存在鲁棒性较差、适应度与计算效率均偏低的问题,文中基于YOLOv3提出了一种轻量化的遥感图像数据分析算法。该算法使用YOLOv3作为神经网络模型的框架,并将内部的Darknet-53多尺度卷积作为主网络。为了减小...针对传统遥感图像数据分析算法存在鲁棒性较差、适应度与计算效率均偏低的问题,文中基于YOLOv3提出了一种轻量化的遥感图像数据分析算法。该算法使用YOLOv3作为神经网络模型的框架,并将内部的Darknet-53多尺度卷积作为主网络。为了减小主网的冗余度,通过SE-Net模型连接网络的深层与浅层卷积,在轻量化的同时也增强了模型的深度特征提取能力。同时,根据改进后网络的权重输出结果,采用剪枝算法对卷积核进行简化,进而完成了模型的轻量化。在实验测试中,轻量化后的模型可显著提升FPS(Frames Per Second)值,且算法的mAP指标为93.25%,在对比算法中为最优,表明了算法模型的有效性及其性能的优越性。展开更多
Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their ...Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.展开更多
文摘Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.
基金supported by the Opening Project of the Key Laboratory of Agri-Informatics,Ministry of Agriculture of China(2012004)the National Basic Research Program of China(973 Program,2010CB951500)+2 种基金the Innovation Project of Chinese Academy of Agricultural Sciencesthe National Natural Science Foundation of China(41301365)the National High-Tech R&D Program of China(863 Program,2013AA12A401)
文摘Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.
文摘Water is an essential natural resource without which life wouldn’t exist.The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district,Tamil Nadu,India,using analytic hierarchy process(AHP)model.Remote sensing and magnetic parameters have been used to determine the evaluation indicators for groundwater occurrence under the ArcGIS environment.Groundwater occurrence is linked to structural porosity and permeability over the predominantly hard rock terrain,making magnetic data more relevant for locating groundwater potential zones in the research area.NE-SW and NW-SE trending magnetic breaks derived from reduction to pole map are found to be more significant for groundwater exploration.The lineaments rose diagram indicates the general trend of the fracture to be in the NE-SW direction.Assigned normalised criteria weights acquired using the AHP model was used to reclassify the thematic layers.As a result,the taluk’s low,moderate,and high potential zones cover 25.08%,25.68%and 49.24%of the study area,respectively.The high potential zones exhibit characteristics favourable for groundwater infiltration and storage,with factors as gentle slope of<3°,high lineament densities,magnetic breaks,magnetic low zones as indicative of dykes and cracks,lithology as colluvial deposits and land surface with dense vegetation.The depth of the fracture zones was estimated using power spectrum and Euler Deconvolution method.The groundwater potential mapping results were validated using groundwater level data measured from the wells,which indicated that the groundwater potential zoning results are consistent with the data derived from the real world.
基金supported by National Natural Science Foundation of China(No.60972141).
文摘The China Remote Sensing Satellite Ground Station was established in 1986.It currently has three receiving stations in the north,west,and south of China,with the capacity to receive data from 15 international and domestic Earth observation satellites covering the entire Chinese territory and 70%of Asia.Meanwhile,a systematic,integrated,and standardized spatial information service system has been built.A data-sharing project for medium-resolution Earth observation satellites has been conducted and plays an important role in land,ocean,and atmospheric resource investigation and environmental monitoring.
文摘针对传统遥感图像数据分析算法存在鲁棒性较差、适应度与计算效率均偏低的问题,文中基于YOLOv3提出了一种轻量化的遥感图像数据分析算法。该算法使用YOLOv3作为神经网络模型的框架,并将内部的Darknet-53多尺度卷积作为主网络。为了减小主网的冗余度,通过SE-Net模型连接网络的深层与浅层卷积,在轻量化的同时也增强了模型的深度特征提取能力。同时,根据改进后网络的权重输出结果,采用剪枝算法对卷积核进行简化,进而完成了模型的轻量化。在实验测试中,轻量化后的模型可显著提升FPS(Frames Per Second)值,且算法的mAP指标为93.25%,在对比算法中为最优,表明了算法模型的有效性及其性能的优越性。
文摘Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.
