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深度学习在高分辨率遥感影像冬油菜提取中的应用 总被引：2，自引：0，他引：2
近年来，深度学习在基于高分辨率遥感影像的农作物种植信息提取领域应用广泛。本文充分利用油菜在盛花期的光谱特征，提出了基于深度学习理论的单时相高分辨率遥感影像油菜分布提取方法。以2016年湖北省沙洋县作为研究区域，获取油菜盛花时期高分一号（GF-1）影像，并以沙洋县为基础影像，通过手工标记制作油菜训练样本。设计两种深度学习框架模型，一种以卷积神经网络（CNN）为框架，构建一维卷积神经网络（1D-CNN）模型，第二种以循环神经网络（RNN）为框架，组合门控循环单元（GRU）模型，训练标准样本模型，完成油菜分类提取。最后，与传统支持向量机（SVM）、随机森林（RF）方法进行了结果对比。试验结果表明，本文设计的基于深度学习CNN和RNN模型提取的冬油菜空间分布精度和面积精度皆优于其他两种方法，为进一步实现冬油菜提取自动化提供试验基础。 相似文献
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高分辨率遥感影像中耕地特征复杂，人工目视解译和传统的遥感影像分类方法提取能力有限，无法实现大范围的自动化高精度耕地提取。深度学习技术因具有较强的地物表达能力，在遥感影像信息自动提取方面表现出了优越的性能，为大范围耕地的精细化自动提取提供了新的思路。探究不同典型网络模型在不同景观特征耕地提取上的适用情况对耕地提取质量和效率的提升具有重要意义。基于此，本研究以高分一号及高分二号融合的2 m分辨率数据为数据源，采用改进的金字塔场景解析网络MPSPNet(Modified Pyramid Scene Parsing Network)和UNet网络模型，应用于山东省的耕地精细自动化提取，并与传统面向对象的方法对比，探究两种深度卷积神经网络模型在大尺度耕地自动提取中的适用性。研究获得以下结论：（1） MPSPNet模型和UNet模型在区/县尺度的耕地提取上性能优于传统的面向对象的分类方法，在全省尺度的耕地提取上总体精度优于90%且无明显差异。（2）耕地景观特征是影响两模型耕地提取效果的重要因素，模型的选择对耕地提取效果无明显影响。在耕地景观指数较低的地块规则平整的区域，模型提取效果较好，在耕地景观... 相似文献
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为研究我国首颗携带红边波段的高分六影像（GF-6）在林地与非林地上的识别贡献，本文选择复杂林地类型的安徽省黄山市作为研究区，采用特征优选（RFE）与随机森林（RF）相结合的方法开展了林地与非林地识别潜力研究。首先根据实地调查、Google Earth影像及林地"一张图"样本数据构建了样本库；然后基于DEM、多时相光谱特征、植被指数、红边指数等特征开展分类，并比较不同模型精度及不同变量的重要度。结果表明：GF-6红边信息对林地非林地识别较为重要，引入红边信息可将总体分类精度提升2%，其他新增波段及地形特征对林地与非林地识别贡献并不明显；多时相数据的运用相比单时相数据可整体提高林地类型的分类精度2.93%~4.1%，单时相分类结果6月最好，9月次之，12月最差；特征优选可以有效减少数据输入维数（46到15），并取得最高分类精度，在不牺牲精度的同时保证了运算数据量的减少且明确了不同变量的贡献，具有较强的应用意义。 相似文献
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GF-2影像面向对象典型城区地物提取方法 总被引：5，自引：3，他引：2
国产高分遥感影像信息丰富,提供了精准的地物空间细节,深入研究高分数据处理及其提取城区地类目标信息的方法具有重要意义。本文以国产高分二号（GF-2）遥感影像为数据源,利用规则集的面向对象分类方法,通过ESP尺度分析工具选取得出最优分割尺度,建立各类地物的特征体系及分类规则,最终提取出研究区典型城区地物信息,并将之与传统基于像元的SVM监督分类结果作比较。结果表明：规则集的面向对象分类总体精度为92.23%,Kappa系数为0.9,比SVM监督分类有大幅度提高。对高分二号等高分辨率影像,面向对象的分类方法精度更高,图示效果更好,是城区地物提取的有效方法。 相似文献
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《现代测绘》2020,(1)
针对国产高分辨率遥感数据在城市绿地信息提取中分割尺度选择问题,选取国产高分一号(GF-1)和中巴地球资源卫星04星(CBERS-04)遥感数据,在数据融合的基础上,采用控制变量法选取影像分割与合并尺度进行绿地信息提取,通过信息提取精度评价确定最优分割尺度。实验结果表明,对于GF-1和CBERS-04国产遥感数据,面向对象的方法均优于基于像元的方法,其中5m分辨率CBERS-04数据,面向对象方法绿地提取精度为90.53%,基于像元方法绿地提取精度为86.54%,推荐分割尺度与合并尺度为(25,70);2m分辨率GF-1数据,面向对象方法绿地提取精度为97.09%,基于像元方法绿地提取精度为83.49%,推荐分割尺度与合并尺度为(45,80)。研究结果能够为国产高分遥感数据城区绿地信息提取和地物分类过程中尺度选择提供借鉴和支持。 相似文献
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植被覆盖度FVC (Fractional Vegetation Cover)是衡量地表植被状况的重要指标之一。卫星遥感是获取全球与区域动态FVC的主要技术手段,但现有FVC产品空间分辨率主要为中等（300 m）及中低分辨率,难以满足应用需求。已有高分辨率卫星较长的重访周期是制约高分辨率FVC产品难以生产的主要原因。国产高分一号（GF-1）宽幅相机（WFV）具有高时空分辨率特点,16 m空间分辨率和4天重访周期为高时空分辨率FVC产品生产提供数据支撑。本文对中国2018年—2020年16 m/10天GF-1 FVC产品从直接验证和间接验证两方面进行定量分析与评价,结果显示：（1）基于中国甘肃黑河站、吉林净月潭站、河北塞罕坝站等地面实测数据进行直接验证,GF-1 FVC产品精度（R2=0.57,RMSE=0.12,BIAS=-0.03）优于GEOV3 FVC产品,能够降低森林类型高估现象;（2）中国陆地近88%的像元GF-1 FVC产品全年缺失率低于70%,在生长季内约占82.73%的像元缺失率低于73.68%,产品能够较好的体现植被季节变化特征。基于国产GF-1卫星的高时空分辨率FVC产... 相似文献
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遥感技术已广泛地应用于土地覆盖/土地利用分类中。在专题应用中,用户只对某一类地物感兴趣,例如耕地提取,即单类别分类问题。随着影像分辨率的提高,基于像元的分类算法难以满足高分辨率影像高精度信息提取的需求。本文采用结合面向对象分类思想和基于正样本、未标记样本遥感单值（PUL）分类方法从多源高分辨率影像中提取耕地信息,并与基于像素的分类实验进行对比分析。结果表明,在缺少部分地类的不完全训练集下,基于面向对象的单值分类较传统神经网络分类有更较稳定的表现,并且远优于基于像素的分类结果。 相似文献
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With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data. 相似文献
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高分辨率卫星的幅宽一般很小,受云雨和轨道回访周期影响,单颗卫星的短时段内覆盖能力有限,因此,单一高分辨率卫星常常无法满足一定时段内的黑臭水体监测需求,需要多源卫星协同监测黑臭水体。为了分析多源高分辨率影像对黑臭水体遥感监测的适用性,本文基于地物光谱仪实测的水体遥感反射率数据,以GeoEye-1、WorldView-2、北京二号(DMC3)、高景一号SV1(SuperView-1)以及GF-PMS系列(GF-1/1B/1C/1D、GF-2、GF-6)传感器波段进行等效计算,结果表明:(1)采用反射率比值模型——BOI(Black and Odorous water Index)模型,GeoEye-1、WorldView-2、SuperView-1和GF-1/1B/1C/1D/2/6影像识别黑臭水体正确率均较高,分别为89.5%、89.5%、92.1%和92.1%。(2)BOI模型不适用于DMC3,这里采用了归一化水体指数NDWI≤0.55判别黑臭水体,识别正确率为89.5%。(3)BOI模型应用于仅有的2景同步卫星影像——GF-2影像,经实测数据验证,识别精度为83.3%,精度较高。针对通州区内的某重叠区,2016年—2021年10颗多源卫星影像协同观测的结果一致性较好,表明了多源遥感影像监测黑臭水体的适用性较好。综合考虑卫星影像空间分辨率和采购成本,给出了合理的协同观测建议。 相似文献
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ABSTRACTImpervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling. 相似文献
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Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world. 相似文献
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森林植被碳储量的空间分布格局及其动态变化是陆地生态系统碳收支核算的基础。作为森林地上生物量的重要指示因子,森林高度的精确估算是提高森林植被碳储量估算精度的关键。现有研究已证明,由专业星载摄影测量系统获取的立体观测数据可用于森林高度提取,但光学遥感数据最大的问题是受云雨等天气因素的影响严重。区域森林地上生物量产品的生产需要充分挖掘潜在数据源。国产高分二号卫星(GF-2)虽然不是为获取立体观测数据而设计的专业星载摄影测量系统,但其获取的图像空间分辨率可达0.8 m,且具备±35°的的侧摆能力,在重复观测区域可构成异轨立体观测。本文以分别获取于2015年6月20日和2016年7月19的GF-2数据作为立体像对,其标称轨道侧摆角分别为0.00118°和20.4984°,以激光雷达数据获取的林下地形(DEM)和森林高度(CHM)为参考,对利用GF-2立体观测数据进行森林高度提取进行了研究。通过对立体处理得到的摄影测量点云的栅格化得到DSM,以激光雷达数据提供的DEM作为林下地形,得到了GF-2的CHM。结果表明GF-2提取的CHM与激光雷达CHM空间分布格局较为一致,两者之间存在明显的相关性,像素对像素的线性相关性(R2)达到0.51,均方根误差(RMSE)为3.6 m。研究结果表明,在林下地形已知的情况下,GF-2立体观测数据可用于森林高度估算。 相似文献
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Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery 总被引：1，自引：0，他引：1
Xiaochun Zhang Qinxue Xiong Liping Di Junmei Tang Jin Yang Huayi Wu 《International Journal of Digital Earth》2018,11(12):1219-1240
Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas. 相似文献
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林顺海 《测绘与空间地理信息》2017,(6):86-88
鉴于采用传统方法在提取高分辨率影像水域信息存在不足,本文以高分辨率国产卫星高分一号宽幅16 m空间分辨率多光谱影像为数据源,以浙江省钱塘江流域为研究区域,通过预先对影像进行NDWI处理,在此基础上利用改进FCM聚类算法实现了水域信息的自动提取,并与传统的NDWI和ISODATA分类方法提取结果进行比较。结果表明,该方法在分类效果和精度方面均优于传统分类方法,作为国产高分一号影像水域信息自动提取方法是可行的。 相似文献
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With the increasing availability of high-spatial-resolution remote sensing imageries and with the observed limitations of pixel-based techniques, the development and testing of geographic object-based image analysis (GEOBIA) techniques for image classification have become one of the main research areas in geospatial science. This paper examines and compares the classification performance of a pixel-based method and an object-based method as applied to high- (QuickBird satellite image) and medium- (Landsat TM image) spatial-resolution imageries in the context of urban and suburban landscapes. For the pixel-based classification, the maximum-likelihood supervised classification approach was employed. And for the object-based classification, the pixel-based classified maps were integrated with a set of image segments produced using various calibrations. The results show evidence that the object-based method can produce classifications that are more accurate for both high- and medium-spatial- resolution imageries in the context of urban and suburban landscapes. 相似文献
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高分六号宽幅多光谱数据人工林类型分类 总被引：1，自引：0，他引：1
高分六号(GF-6)卫星于2018年成功发射,2019-03正式投入使用。由于GF-6宽幅相机的WFV(Wide Field of View)影像较GF-1的同类影像新增2个红边波段,将会提高对农业、林业、草原等资源监测能力。为了分析GF-6的WFV影像在人工林分类方面的能力,促进高分数据在林业领域的应用,本文选取广西高峰林场为研究区,以最新的GF-6 WFV影像为数据源,结合地面实测类型数据,进行广西南宁高峰林场的桉树,杉木等人工林类型提取。主要运用随机森林(random forests)的分层分类法:首先计算6种植被指数,并利用随机森林法进行植被指数的特征优选,然后确定4种波段组合数据集的分类方案,4种数据集分别为(1)无红边的前4个波段,(2)有红边的8个波段,(3) 8个波段加上未优化的植被指数特征组合,(4) 8个波段加上优化的植被指数特征组合。再进行WFV影像4种数据集的随机森林分类,随机森林采用分类回归树(CART)算法来生成分类树,结合了bagging和随机选择特征变量的优点,是一种有效的分类方法。最后比较4个方案的分类结果并进行精度验证。结果表明:方案2比方案1精度提高了4.99%,Kappa系数提高了0.058。说明包含红边的8波段数据比4个波段数据精度有大幅提升。方案4的8波段加上优化植被指数特征组合的分类精度最高,达到了85.38%,比方案2包含红边波段组和方案1无红边波段组的精度分别提高了3.98%,8.97%,Kappa系数分别提高了0.046,0.104。说明WFV影像加入红边波段比无红边波段精度明显增高。由结果可知,红边指数的引入,增强了植被信息,能够较准确地反映人工林类型特征差异,明显提升了人工林的分类精度。本研究方法可以有效改善广西人工林类型信息提取效果,为GF-6影像质量的评价及其在林业应用潜力提供科学参考依据。 相似文献