用多角度POLDER数据验证互易原理在遥感像元尺度的适用性

利用ADEOS POLDER多角度遥感数据 ,从统计意义出发 ,对光照与观测角度互易的观察对进行考察 ,发现在POLDER数据中存在不可忽略的不互易现象 ,以文中定义的不互易度来衡量 ,可见光波段不互易度可达 1.0 ,红外波段不互易度可达 0 .3。两个方向上计算NDVI的偏差可达 0 .2。分析了不互易度与NDVI和海拔的相关性 ,发现不互易性与地表的复杂程度有一定的相关性 ,并提出地物光谱反射弱的数据不互易度大的假设。

三维结构真实遥感像元场景的生成

遥感像元场景模型是遥感机理研究的关键组成 ,通过调查光与遥感像元场景的相互作用 ,可以帮助人们理解遥感信号产生的机理 ,并验证遥感物理模型 .从植被野外测量、三维结构真实遥感像元场景的参数化描述、场景数据结构、场景生成方法和流程等方面阐释了遥感像元场景模型 .植被的野外测量和统计是结构真实场景的基础 ;场景生成方法为 :( 1 )使用L系统生成结构真实植株 ,进而生成遥感像元场景 ;( 2 )按统计规则直接生成遥感像元场景 .试验表明该遥感像元场景模型可以生成符合遥感像元统计规律的三维场景 ,是准确和便捷地计算光与植被相互作用的可靠基础 .

山地生态系统通量足迹遥感像元尺度空间代表性分析

遥感技术是大尺度上估算地表碳、水和能量等通量的重要信息来源。全球涡度通量观测数据集已广泛用于遥感通量数据产品的生产和评价,因此,科学评估遥感像元尺度上通量足迹的空间代表性对于遥感通量产品建模、验证和生产尤为重要。本文选择中国西南典型山地生态系统—王朗山地遥感四川省野外观测研究站(简称王朗站)区域为研究对象,使用二维参数化足迹模型刻画了通量观测足迹的时空变化特征,同时解析了通量观测足迹在多个遥感像元尺度(30 m、60 m、120 m、250 m、500 m、1000 m、1500 m和2000 m)上的空间代表性。结果表明:在通量足迹的空间变化上,王朗站内不同观测塔通量足迹范围跨度较大(10—103 m)且对称性较低(通常在40%以下),因此在山地生态系统进行遥感模型及产品验证时需要更加关注通量足迹的空间代表性差异;在通量足迹的时序变化上,王朗站内日尺度上的足迹重叠性差异明显(0%—88%),结合时序变化的足迹特征可进一步提升高时间分辨率下的模型验证和产品精度;王朗研究区内落叶阔叶灌丛站点、落叶阔叶林站点和常绿针叶林站点等3个观测塔的高度为10 m、30 m和75 m,其分别在30 m、60 m和1000 m像元尺度取得对通量足迹的最佳空间代表性。总之,由于山区通量观测的高空间代表性局限于高空间分辨率(观测高度较低时)和中低空间分辨率(观测高度较高时)的遥感像元,认知通量足迹在不同遥感像元尺度上空间代表性的差异,结合多尺度遥感观测数据和时空尺度扩展方法,可促进山区生态系统参量估算和通量研究。本研究可为站点观测尺度扩展、山地生态系统遥感数据产品生产和地球系统模型验证提供参考。

网格地图与网格计算

源远流长 ,古朴简易的网格地图方法 ,仍可作为空间分析、空间数据挖掘 (SDM)的工具之一。以遥感像元数据为基础 ,结合四分树、三角网等数据处理技术的网格地图获得了新的活力和现代功能。当面临全球互联网走向网格计算新技术的机遇时 ,如果能够将各自优势互补 ,网格地图就将推陈出新 ,生机无限。在数字化城市的应用方面 ,它也有可能与城市规划和建设中的网格化趋向相融合 ,成为“数字城市”

MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

Remote Sensing image fusion is an effective way to use the large volume ofdata from multi-source images. This paper introduces a new method of remote sensing image fusionbased on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectralremote sensing data SPOT-4. Firstly, the new method is established by building a model of remotesensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classificationfusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) Fromsubjectivity assessment, the spatial resolution of the fused image is improved compared to theSPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitativeanalysis, the effect of classification fusion is better. As a whole, the re-suit shows that theaccuracy of image fusion based on SVM is high and the SVM algorithm can be recommended forapplication in remote sensing image fusion processes.

Edge detection method for remote sensing image based on morphological variable structuring element

There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %

Accuracy Analysis on Bundle Adjustment of Remote Sensing Images Based on Dual Quaternion

A bundle adjustment method of remote sensing images based on dual quaternion is presented,which conducted the uniform disposal corresponding location and attitude of sequence images by the dual quaternion.The constraint relationship of image itself and sequence images is constructed to compensate the systematic errors.The feasibility of this method used in bundle adjustment is theoretically tested by the analysis of the structural characteristics of error equation and normal equation based on dual quaternion.Different distributions of control points and stepwise regression analysis are introduced into the experiment for RC30 image.The results show that the adjustment accuracy can achieve 0.2min plane and 1min elevation.As a result,this method provides a new technique for geometric location problem of remote sensing images.

Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing

This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.

A method of remote sensing image water segmentation based on adaptive morphological elliptical structuring elements

The use of visible and infrared remote sensing images to calculate the water area is an effective means to grasp the basic situation of water resources,and water segmentation is the premise of statistics.Generally,the edge features of the water in the remote sensing images are complex.When the traditional morphology is used for image segmentation,it is easy to change the image edge and affect the accuracy of image segmentation because the fixed structuring elements are used to perform morphological operations on the image.To segment water in the remote sensing image accurately,a remote sensing image water segmentation method based on adaptive morphological elliptical structuring elements is proposed.Firstly,the eigenvalue and eigenvector of the image are estimated by linear structure tensor,and the elliptical structuring elements are constructed by the eigenvalue and eigenvector.Then adaptive morphological operations are defined,combining the close operation to eliminate the influence of dark detail noise on water without overstretching the water edge,so that the water edge can be maintained more accurately.Finally,on this basis,the water area can be segmented by gray slice.The experimental results show that the proposed method has higher segmentation accuracy and the average segmentation error is less than 1.43%.