由于热带地区的雨季时间较长,云覆盖严重,基于光学影像难以准确提取区域内的水稻种植模式。该文以泰国湄南河流域中部平原水稻种植区为例,基于Sentinel-1SAR时间序列数据,提出一种融合时序统计参数与时序曲线相似性特征的热带地区水稻...由于热带地区的雨季时间较长,云覆盖严重,基于光学影像难以准确提取区域内的水稻种植模式。该文以泰国湄南河流域中部平原水稻种植区为例,基于Sentinel-1SAR时间序列数据,提出一种融合时序统计参数与时序曲线相似性特征的热带地区水稻种植结构提取方法。首先利用年内所有可获取的Sentinel-1SAR数据,分别基于像元和基于对象构建后向散射系数时间序列曲线,提取时序特征参数;利用动态时间规整(Dynamic Time Warping,DTW)算法,计算后向散射系数时序曲线与地物标准曲线间的隶属度;将时序特征参数、时序曲线隶属度相结合,利用随机森林模型进行机器学习监督分类,提取研究区的水稻种植信息并评价分类精度。结果表明,基于Sentinel-1SAR时序特征融合的算法可以较好地提高水稻种植结构分类精度。其中,基于对象的分类算法的单季稻提取用户精度为81.46%,生产者精度为82.00%;双季稻用户精度为88.0%,生产者精度为84.08%,均优于基于像元的分类算法。研究结果可为多云多雨的热带地区水稻种植信息提取提供一种新的思路。展开更多
针对SAR图像数据集的特点,提出了一种基于像元级图像时间序列相似性的水体提取方法。其基本思想是:构建像元级SAR图像时间序列,选取动态时间归整(dynamic time warping,DTW)算法作为时间序列相似度的度量方法,计算所有像元与标准水体像...针对SAR图像数据集的特点,提出了一种基于像元级图像时间序列相似性的水体提取方法。其基本思想是:构建像元级SAR图像时间序列,选取动态时间归整(dynamic time warping,DTW)算法作为时间序列相似度的度量方法,计算所有像元与标准水体像元时间序列的相似性;将水体边缘混合像元的DTW距离值设定为参考阈值,采用阈值法提取相似性较高的时间序列数据,其对应的像元即被识别为水体像元;最后利用每个像元的DTW距离值代替其后向散射值,采用8邻域搜索方法提高水体识别的精度。以2008年1—12月获取的25景分辨率为150 m的ENVISAT ASAR图像进行水体像元提取试验,结果表明,该方法的完整率和正确率均较高,能够应用于大范围区域水体的提取与制图。展开更多
洪涝灾害对中国沿湖沿江地带的国家经济和人民财产威胁巨大。针对洪涝灾害期间光学影像质量低,单幅SAR (Synthetic Aperture Radar)影像水体提取多依赖经验阈值且可靠性不足等问题,本文提出了一种适应复杂区域的时序SAR影像洪水监测方...洪涝灾害对中国沿湖沿江地带的国家经济和人民财产威胁巨大。针对洪涝灾害期间光学影像质量低,单幅SAR (Synthetic Aperture Radar)影像水体提取多依赖经验阈值且可靠性不足等问题,本文提出了一种适应复杂区域的时序SAR影像洪水监测方法。为获取完整的洪水淹没区域,首先结合影像序列的统计分析结果,设计了两个归一化差异指数——突出临时洪水的淹没范围提取指数SREI (Submerged Range Extraction Index)和突出矮植被覆盖的植被区淹没范围提取指数SRVEI (Submerged Range in Vegetation area Extraction Index);然后,根据同一区域植被季候性分布较为稳定的前提,给出了阈值的自适应选取方式;最后,考虑中国湖泊周围地物特点,构建了适应的后处理过程,优化提取区域,形成了洪水淹没区域提取与监测的详细流程和通用框架。本文以东洞庭湖流域为主要研究区域验证了方法的提取精度,并在此基础上进行了2020年东洞庭湖流域洪涝灾害态势分析和洪水淹没地物分析,展示了方法在洪涝灾害监测评估上的应用性能。另外,本文还将该方法应用于东洞庭湖流域往年数据,进行了汛期洪水淹没范围年际分析,并添加了同年鄱阳湖洪水淹没区域的实验,验证了方法时空应用的稳定性。相关实验结果表明,本文方法对洪水淹没区域的提取精度高,用户依赖性低,可跨越时空地应用于不同洪水淹没区域监测场景,且可以初步区分不同属性的洪水淹没范围,能够为本文涉及区域及其他区域的洪涝灾害监测、评估和预警提供一定的参考。展开更多
Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effecti...Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.展开更多
It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations’ economic development. Majority of agricultural professionals rarely capture the cropping patterns neces...It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations’ economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises.Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center(CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar(SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops(maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double-and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.展开更多
地基合成孔径雷达(Ground Based Synthetic Aperture Radar,GBSAR)是目前露天矿山工作帮及排土场进行亚毫米级形变监测的主要技术手段之一,但监测过程中出现的多径效应造成的差分相位变化会被错误识别为形变。针对识别形变精度低的问题...地基合成孔径雷达(Ground Based Synthetic Aperture Radar,GBSAR)是目前露天矿山工作帮及排土场进行亚毫米级形变监测的主要技术手段之一,但监测过程中出现的多径效应造成的差分相位变化会被错误识别为形变。针对识别形变精度低的问题,本文开展了差分干涉相位时序特征表达方法的研究,并以此为基础提出了一种基于注意力网络模型的地基SAR时序差分相位分类方法,以形变变化趋势与区域范围作为依据来区分突变区域和缓变区域,通过模型预测出真实形变分布。经过实验结果证明,注意力网络模型可以较为准确的提取出形变分布,有效减少多径效应造成的误差干扰。展开更多
文摘由于热带地区的雨季时间较长,云覆盖严重,基于光学影像难以准确提取区域内的水稻种植模式。该文以泰国湄南河流域中部平原水稻种植区为例,基于Sentinel-1SAR时间序列数据,提出一种融合时序统计参数与时序曲线相似性特征的热带地区水稻种植结构提取方法。首先利用年内所有可获取的Sentinel-1SAR数据,分别基于像元和基于对象构建后向散射系数时间序列曲线,提取时序特征参数;利用动态时间规整(Dynamic Time Warping,DTW)算法,计算后向散射系数时序曲线与地物标准曲线间的隶属度;将时序特征参数、时序曲线隶属度相结合,利用随机森林模型进行机器学习监督分类,提取研究区的水稻种植信息并评价分类精度。结果表明,基于Sentinel-1SAR时序特征融合的算法可以较好地提高水稻种植结构分类精度。其中,基于对象的分类算法的单季稻提取用户精度为81.46%,生产者精度为82.00%;双季稻用户精度为88.0%,生产者精度为84.08%,均优于基于像元的分类算法。研究结果可为多云多雨的热带地区水稻种植信息提取提供一种新的思路。
文摘针对SAR图像数据集的特点,提出了一种基于像元级图像时间序列相似性的水体提取方法。其基本思想是:构建像元级SAR图像时间序列,选取动态时间归整(dynamic time warping,DTW)算法作为时间序列相似度的度量方法,计算所有像元与标准水体像元时间序列的相似性;将水体边缘混合像元的DTW距离值设定为参考阈值,采用阈值法提取相似性较高的时间序列数据,其对应的像元即被识别为水体像元;最后利用每个像元的DTW距离值代替其后向散射值,采用8邻域搜索方法提高水体识别的精度。以2008年1—12月获取的25景分辨率为150 m的ENVISAT ASAR图像进行水体像元提取试验,结果表明,该方法的完整率和正确率均较高,能够应用于大范围区域水体的提取与制图。
文摘洪涝灾害对中国沿湖沿江地带的国家经济和人民财产威胁巨大。针对洪涝灾害期间光学影像质量低,单幅SAR (Synthetic Aperture Radar)影像水体提取多依赖经验阈值且可靠性不足等问题,本文提出了一种适应复杂区域的时序SAR影像洪水监测方法。为获取完整的洪水淹没区域,首先结合影像序列的统计分析结果,设计了两个归一化差异指数——突出临时洪水的淹没范围提取指数SREI (Submerged Range Extraction Index)和突出矮植被覆盖的植被区淹没范围提取指数SRVEI (Submerged Range in Vegetation area Extraction Index);然后,根据同一区域植被季候性分布较为稳定的前提,给出了阈值的自适应选取方式;最后,考虑中国湖泊周围地物特点,构建了适应的后处理过程,优化提取区域,形成了洪水淹没区域提取与监测的详细流程和通用框架。本文以东洞庭湖流域为主要研究区域验证了方法的提取精度,并在此基础上进行了2020年东洞庭湖流域洪涝灾害态势分析和洪水淹没地物分析,展示了方法在洪涝灾害监测评估上的应用性能。另外,本文还将该方法应用于东洞庭湖流域往年数据,进行了汛期洪水淹没范围年际分析,并添加了同年鄱阳湖洪水淹没区域的实验,验证了方法时空应用的稳定性。相关实验结果表明,本文方法对洪水淹没区域的提取精度高,用户依赖性低,可跨越时空地应用于不同洪水淹没区域监测场景,且可以初步区分不同属性的洪水淹没范围,能够为本文涉及区域及其他区域的洪涝灾害监测、评估和预警提供一定的参考。
基金Key Program of the National Natural Science Foundation of China (Grant No. U1301253)Guangdong Provincial Science and Technology Project (Nos. 2017A050501031 and2017A040406022)+1 种基金Guangzhou Science and Technology Projects (Nos. 201807010048 and 201804020034)the International Postdoctoral Exchange Fellowship Program 2017 (No. 20170029). The authors would like to express their thanks to European Space Agency for providing Sentinel-1 SAR data as well as ESA-SNAP software in conducting research, our colleagues Haiyan Deng and Li Zhao for their assistance in collecting field validation, and processing images, and the colleagues from the Guangzhou Urban Renewal Bureau for their good suggestions. We also would like to thank the editors and anonymous reviewers for their instructive comments.
文摘Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.
基金Under the auspices of Fundamental Research Funds for the Central Universities,China(No.2017TD-26)the Plan for Changbai Mountain Scholars of Jilin Province,China(No.JJLZ[2015]54)
文摘It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations’ economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises.Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center(CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar(SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops(maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double-and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.
文摘地基合成孔径雷达(Ground Based Synthetic Aperture Radar,GBSAR)是目前露天矿山工作帮及排土场进行亚毫米级形变监测的主要技术手段之一,但监测过程中出现的多径效应造成的差分相位变化会被错误识别为形变。针对识别形变精度低的问题,本文开展了差分干涉相位时序特征表达方法的研究,并以此为基础提出了一种基于注意力网络模型的地基SAR时序差分相位分类方法,以形变变化趋势与区域范围作为依据来区分突变区域和缓变区域,通过模型预测出真实形变分布。经过实验结果证明,注意力网络模型可以较为准确的提取出形变分布,有效减少多径效应造成的误差干扰。
