共查询到20条相似文献，搜索用时 31 毫秒
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The feasibility of differentiating four oil-seed crops viz., mustard, toria, yellow sarson and sunflower, based on their spectral reflectance in the visible and near infra red region was studied in a field experiment, The spectral vegetative index profiles, generated during the growth period of different oil seed crops indicated two vegetative growth peaks and a depression between the two peaks, due to the conspicuous yellow colour of flowers, which masked the green leaves. The magnitude of such depression in the spectral vegetation indices viz., ‘Greenness’ and ‘Perpendicular Vegetation Index’ (PVI), were of higher magnitude in yellow sarson. The flowering period parameters viz., flowering time, duration and intensity, deduced from the spectral vegetation indices were found to be beneficial in differentiating different oil-seed crops by remote sensing. A plot of ‘Brightness’ vs. ‘Greenness’ values determined during the growth of the crops formed typical clusters. The cluster representing toria crop was significantly different from the other crops, thereby making toria identifiable from others by remote sensing. 相似文献
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极地区域气溶胶地基遥感观测及分析 总被引：1，自引：0，他引：1
依据全球气溶胶自动观测网在极地区域的观测,对以下问题进行了讨论:(1)温度订正在极地气溶胶光学厚度观测中的重要性及订正方法;(2)在极地进行仪器常数标定的可行性分析及温度效应的影响;(3)利用天空散射光观测反演气溶胶微物理和光学特性参数(粒子谱分布、散射相函数、复折射指数、单次散射反照率等)时,关于观测几何选择的问题,以及针对极地区域气溶胶特性的初步反演结果。这些研究可以为中国未来在两极地区开展基于自动太阳-天空辐射计的气溶胶地基遥感观测提供参考,为极地区域气溶胶卫星遥感及气候效应评估等研究提供重要支撑。 相似文献
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微波植被指数在干旱监测中的应用 总被引：3，自引：0，他引：3
在植被覆盖区域,归一化植被指数(NDVI)被广泛地应用于干旱遥感监测。和基于光学遥感的植被指数相比,Shi等提出的微波植被指数MVI(Microwave Vegetation Index)被证实能够反映更多的植被生长信息。本文以MVI为基础,利用MVI代替目前比较成熟的温度植被指数TVDI(Temperature Vegetation Index)中的NDVI,构建温度微波植被干旱指数TMVDI(Temperature Microwave Vegetation Index),发展了一种新的干旱监测方法。本文以2006年夏季四川省发生的百年难遇的干旱为研究对象,将基于TMVDI与TVDI的干旱监测结果进行了对比分析。最后,为评估监测结果的准确性,将遥感监测的结果与基于气象站点降雨观测数据构建的标准降雨指数SPI(Standardized Precipitation Index)的计算结果进行了对比分析。结果表明,利用低频降轨微波辐射计数据计算的T MVDI最适合于进行植被覆盖区域的干旱监测。 相似文献
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张楠楠 《测绘与空间地理信息》2012,35(9):69-73
利用遥感技术进行农作物识别和监测是遥感应用领域的重要研究内容之一。以2006—2007年两个时相的CBERS-02 CCD影像为主要遥感数据源,对山东省某市的主要农作物的种植分布情况进行监测。将农作物的物候特征、光谱特征和纹理特征及GIS辅助信息等多源信息融合,建立识别知识规则,通过知识推理,逐步识别出冬小麦、夏玉米和棉花。最后,利用混淆矩阵对实验结果进行验证。通过分析比较,证明上述方法在监测作物空间分布方面具有较高精度。 相似文献
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本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法,以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像,利用其质量评定波段完成研究区无云时相及区域上的地块统计,提取地块级别的各波段反射率均值按照时相顺序及波长进行排列,构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN（Convolutional Neural Network）结构完成对特征图的分类,从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求,可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较,获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力,应视为基于地块的多时相影像分类任务的优选方法。 相似文献
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南方平原耕地具有地块破碎、农作物种植品种多且空间分布混杂程度高等特点,运用传统的遥感技术方法精确监测农作物面积较为困难。无人机航拍具有拍摄时间灵活、空间分辨率高、成本低等优势,为解决这一难题提供了有利途径。本文通过地面样地调查,获取杭州市余杭区瓶窑镇农作物样地的位置及种植品种数据,利用面向对象的多尺度分割方法与随机森林的分类方法对无人机航拍数据进行分割、分类,深入挖掘高分辨率遥感数据信息,用于提取农作物种植品种及其空间分布信息,实现高精度的农作物种植面积遥感监测,推进无人机遥感在农业中的深入应用,提高农业遥感应用效益。 相似文献
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U. P. Shahi Suman Kumar N. P. Singh A. K. Chaubey Yogesh Kumar 《Journal of the Indian Society of Remote Sensing》2007,35(1):53-65
The potential usefulness of spectral properties and vegetation indices in varietal discrimination of potato genotypes was
studied in the field experiment. Spectral measurements were recorded in different bands in blue (450–520 nm), green (520–590
nm), red (620–680 nm) and infrared (770–860 nm) of the electromagnetic spectrum at different stages during crop growth period.
A ground based hand held multiband radiometer (Model/041) was used for the purpose. The mean per cent green reflectance value
among different genotypes was lowest in genotype MS/86-89, while it was observed highest in genotype JX-216. Significant difference
among these genotypes was found at all growth stages except 6 week after planting. Consequent to variation in spectral reflectance
the vegetation indices like, NDVI, RVI, TVI and DVI showed significant difference among genotypes at all growth stages except
at 8th week after planting. The vegetation indices are good indicators of crop growth and condition. Similarly, fresh weight, dry
weight, and leaf area index were also highest in MS/86-89, followed by KUFRI Bahar and KUFRI Sutlej while in case of leaf
area index it was followed by Kufri Sutlej and Kufri Bahar. JX-23 was highest in chlorophyll content and tuber yield followed
by MS/86-89 and JW-160, while lowest chlorophyll content was seen in MS/89-1095 and poorest tuber yield in MS/89-60. Most
of the genotypes exhibited considerable variation in their spectral response and vegetation indices thereby indicating the
possibility of their discrimination through remote sensing technique. 相似文献
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Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物 总被引：2，自引：0，他引：2
本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。 相似文献
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Ramesh Kestur Akanksha Angural Bazila Bashir S. N. Omkar Gautham Anand M. B. Meenavathi 《Journal of the Indian Society of Remote Sensing》2018,46(6):991-1004
UAVs are fast emerging as a remote sensing platform to complement satellite based remote sensing. Agriculture and ecology is one of the important applications of UAV remote sensing, also known as low altitude remote sensing (LARS). This work demonstrates the use and potential of LARS in agriculture, particularly small holder open field agriculture. Two UAVs are used for remote sensing. The first UAV is a fixed wing aircraft with a high spatial resolution visible spectrum also known as RGB camera as a payload. The second UAV is a quadrotor UAV with an RGB camera interfaced to an on-board single board computer as the payload. LARS was carried out to acquire aerial high spatial resolution RGB images of different farms. Spectral–spatial classification of high spatial resolution RGB images for detection, delineation and counting of tree crowns in the image is presented. Supervised classification is carried out using extreme learning machine (ELM), a single hidden layer feed forward network neural network classifier. ELM was modelled for RGB values as input feature vectors and binary (tree and non-tree pixels) output class. Due to similarities in spectral intensities, some of the non-tree pixels were classified as tree pixels and in order to remove them, spatial classification was performed on the image. Spatial classification was carried out using thresholded geometrical property filtering techniques. Threshold values chosen for carrying out spatial classification were analysed to obtain optimal values. Finally in the delineation and counting, the connected tree crowns were segmented using Watershed algorithm performed on the image after marking individual tree crowns using Distance Transform method. Five representative UAV images captured at different altitudes with different crowns of banana plant, mango trees and coconut trees were used to demonstrate the performance of the proposed method. The performance was compared with the traditional KMeans spectral–spatial method of clustering. Results and comparison of performance parameters of KMeans spectral–spatial and ELM spectral–spatial classification methods are presented. Results indicate that ELM performed better than KMeans. 相似文献
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通过对遥感影像中河流光谱特征的研究,提出了一种基于区域生长的遥感影像河流提取方法。该方法首先根据遥感影像生成灰度图像,选取河流生长初始区域,设置生长范围和生长阈值,通过区域生长得到河流边界,剔除河流内部"孤岛"并对边界细化,最后生成矢量文件。本文使用具有不规则河流边界的Landsat8遥感影像作为实验对象,验证方法的可行性和有效性。 相似文献
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Jesús Delegido Luis AlonsoGonzalo González José Moreno 《International Journal of Applied Earth Observation and Geoinformation》2010
The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm2, similar or smaller than other methods but with the improvement of applicability to a large set of different crop types. 相似文献
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S. S. Ray Anil Sood Gargi Das Sushma Panigrahy P. K. Sharma J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2005,33(2):181-188
In this study, an attempt has been made to suggest crop diversification based on soil and weather requirements of different
crops. State level spatial databases of various agro-physical parameters such as rainfall, soil texture, physiography and
problem soil along with the agricultural area derived from remote sensing data were integrated using GIS. A raster based modelling
approach was followed to arrive at suitable zones for practicing different cropping systems. The results showed that the south-western
Punjab is suitable for low water requiring crops such as desi cotton, pearl millet, gram etc., where as north-eastern Punjab
with high rainfall and excess drainage should practice maize based cropping system. Rice can be substituted by maize and other
crops in Central Punjab, where water table is going down fast. Using this approach the area of rice based cropping system
can be reduced from present 24.7 lakh ha to 19.6 lakh ha, thereby reducing the degradation of valuable land and water resources. 相似文献
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土地利用/覆被专题信息的快速、高效、准确提取是遥感图像处理研究的重要方向。传统的遥感分类方法常依靠像元的光谱值,未充分利用影像的空间信息。本文将面向对象影像分割和支持向量机方法相结合,复合光谱和纹理信息,建立了Object-SVM分类模型,并与面向对象的模糊函数和基于像元的SVM方法相比较,探寻区域尺度土地利用/覆被信息提取方法。结果显示,Object-SVM模型有效地提高了遥感图像的分类精度和分类效率,对于区域尺度影像的快速、准确、客观的信息提取具有实际意义。 相似文献
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利用Sentinel-2A数据提取长江中下游丘陵地带农作物种植信息 总被引：1，自引：1，他引：0
长江中下游丘陵地带地块细小破碎、种植结构复杂,导致作物遥感光谱特征相互纠缠,信息精确提取困难等。本文基于Sentinel-2A数据提出了多特征组合优化的丘陵地带农作物种植结构精确识别方法。首先获取研究区内主要农作物的关键物候特征信息;然后计算其光谱特征、纹理特征、地形特征值,构建原始特征集;最后采用随机森林方法对特征进行重要性排序,对原始特征集进行特征变量优化,并选择优化后的组合特征进行监督分类提取出研究区农作物信息。试验结果表明,相较于单变量特征,通过多特征优化组合分类总体精度和Kappa系数分别从80.4%和0.748提高到96.3%和0.954,有效地提高了南方丘陵地带农作物分类精度,算法稳定性较强。在南方丘陵地带农作物的识别过程中,进行特征变量优化后的地形特征与纹理特征能显著提高分类精度。 相似文献
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Mixed pixel is a key issue in medium to coarse resolution remote sensing image, and it seriously restricts the remote sensing classification. This paper presents an Independent component analysis (ICA) algorithm based on the variational Bayesian (VB) methods, named VBICA, for spectral unmixing in multispectral remote sensing image. The model assumes that the mixed pixels to be separated are given as linear mixtures. The matrixes of linear mixtures are assumed to be unknown. In the Bayesian framework, the endmember and abundance have finally been achieved with Bayesian inference and approximate variational algorithm. The proposed method is evaluated and tested on a numerical simulative image from the noise resistance, area size, pixel purity, estimated number of endmembers and real multispectral remote sensing image of 100?×?100 pixels. Experimental results on simulated image demonstrated that compared to the Fast ICA algorithm, the proposed algorithm can give more accurate results, and the validity of the proposed algorithm is verified by the real multispectral remote sensing image of the similarity on spectral curves, average similarity and ground objects distribution maps. 相似文献
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闪电河流域水循环和能量平衡遥感综合试验 总被引：3，自引：3，他引：0
赵天杰 施建成 徐红新 孙彦龙 陈德清 崔倩 贾立 黄硕 牛升达 李秀伟 阎广建 陈良富 柳钦火 赵凯 郑兴明 赵利民 郑超磊 姬大彬 熊川 王天星 李睿 潘金梅 闻建光 穆西晗 余超 郑姚闽 蒋玲梅 柴琳娜 卢麾 姚盼盼 马建威 吕海深 武建军 赵伟 杨娜 郭鹏 李玉霞 胡路 耿德源 张子谦 胡建峰 杜爱萍 《遥感学报》2021,25(4):871-887
遥感试验是进行遥感原理的验证、遥感模型与反演方法的发展、遥感产品的真实性检验,推动卫星计划的论证实施及其观测在地球系统科学中应用的重要途径。闪电河流域水循环和能量平衡遥感综合试验以滦河上游闪电河流域为核心试验区,以地球表层系统的水循环过程和能量平衡为研究对象,旨在通过天—空—地一体化的观测手段,针对不同典型地表类型开展全波段主被动协同遥感观测,研究异质地表和山地条件下像元尺度遥感关键参量的观测方案,研究重要水热参量的遥感方法及其同陆面/水文过程模型的结合,支撑国家民用空间基础设施和空间科学先导专项相关卫星计划的论证实施。其中,航空飞行遥感试验搭载L波段主被动一体化微波载荷、双角度热红外相机、四波段多光谱相机和高光谱成像仪进行协同观测,实现了土壤水分、组分温度、植被含水量、叶面积指数等地表参数以及湖泊、水库、湿地等的遥感监测;地面同步观测试验利用车载微波辐射计、地基雷达和光谱仪进行了典型地物如裸土、植被、水体、人工目标等的遥感观测,并按照样区—样方—样点的多尺度嵌套方案进行了地表参数的同步采样,获取了该地区关键地表参数的短时期时空变化特征;同时配合卫星和机载观测,在闪电河流域完成了土壤温湿度、地表水热通量、地表辐射四分量、降水等气象要素的地面观测网络的建设,为验证地表辐射/散射遥感模型,发展、优化和验证水热参量遥感反演算法,研究地表水热参量尺度效应与尺度转化问题提供了重要平台,将促进陆表能量与水分交换过程的理解及其对全球变化的作用和反馈机制的研究。 相似文献