遥感影像亚像元制图方法研究进展综述

遥感影像混合像元的普遍存在给遥感影像解译造成困扰。有效处理混合像元问题,细化分类结果,获得更为精细的地物细节信息就需要进行亚像元绘图。目前亚像元制图方法主要包括3个步骤:① 混合像元分解;② 提取软信息;③ 亚像元制图。总结归纳了近年来遥感影像亚像元绘图领域的研究进展和成果,详细阐述了亚像元制图的步骤及涉及的研究方法。依据辅助信息的类型将亚像元绘图方法大致划分为:基于空间相关性、基于空间结构信息、基于神经网络、基于像元交换途径的4类亚像元分类方法,并分别对各种方法的优缺点进行了分析对比。最后,评述了亚像元制图的发展趋势。     

基于改进SAM的高光谱影像混合像元分解算法研究

将传统遥感图像分类方法中的光谱角度制图法(Spectral Angle Mapping-SAM)加以变换,改进为一种符合全约束条件下的高光谱遥感图像的混合像元分解模型.新算法在端元丰度比例满足全约束的条件下,通过逼近的方法寻找一种端元丰度的比例组合,使测试光谱与目标光谱的广义夹角最小,从而认为该比例组合就是混合像元分解...     

遥感影像超分辨率制图研究进展

尺度问题是土地覆盖分类中的一个核心问题,向下尺度转换又是其中的难点。混合像元分解可以得到亚像元尺度的类别组分百分比,但无法求得亚像元的具体位置。遥感影像超分辨率制图是由粗空间分辨率的影像得到高空间分辨率分类结果图的技术,可用于地表分类向下尺度转换,近年来该技术已成为遥感影像分类和尺度转换领域的研究热点。对超分辨率制图研究进展做了详细论述,从超分辨率制图的发展和研究现状、主要方法、精度评价等几方面进行了详细阐述,并分析了当前超分辨率制图算法存在的主要问题,以及可能的研究重点和发展空间。     

3 种子像元分解分类的对比研究-用NOAA 影像进行鄱阳湖洪水监测为例

子像元分解分类是遥感领域比较棘手的方法, 尽管目前有不少软件和算法可以进行这方面的处理, 但对其结果的应用还处在初级探索阶段。通过回顾目前用于解决像元分解问题的基本理论和方法, 介绍了当今用于子像元分解分类的3 种技术方法(即ILW IS3. 0子像元线性分解分类、Erdas8. 5子像元分解分类、eCognition3. 0 最近邻分解分类) 的基本原理、算法、参数设定和计算过程, 以NOAA 影像的第一、第二波段数据(卫星扫描时间是1998 年8 月11) 对江西鄱阳湖的洪水监测为例, 用上述3 种软件和方法进行了实际分解分类计算, 并在文中展示了3 种方法的最后分类结果。其次对3 种分类方法的基本原理、设定参数、适合应用和各自存在缺点进行了理论、应用上的分析对比, 还就子像元分解分类结果(每个像元含有子像元的百分比) 的应用(即子像元的空间分配问题) 提出了个人意见, 以期能为今后遥感同仁在以后的类似具体问题上提供一点参考价值。     

FUZZY-ARTMAP混合像元分解及其应用

对于定量遥感来说,混合像元是制约它深入发展的难点之一。针对中高分辨率图像ETM＋,采用FUZZY ARTMAP神经网络进行混合像元分解,获得像元中不同地物的含量,进而获得像元的植被覆盖度。该方法在某地区ETM+图像的应用表明,试验结果能满足应用需求。     

基于遗传算法的混合像元快速分解及分类算法

在深入研究混合像元分解原理的基础上,提出了用遗传算法进化模型拟合分解结果的超平面,以实现混合像元分解并进一步分类的算法。给出了用该方法对多光谱图像中混合地物进行分类的实例。实验结果证明:该算法的结果与全约束最小二乘(FCLS)的混合像元分解算法结果相近(相关系数达到0.99),而计算复杂度大大降低,计算速度明显提高且具有较强的适应性。同时在整幅遥感图像的分类中体现出较高的分类性能,为遗传算法在混合地物分类问题中的应用提供了又一条可行的途径。     

基于元胞自动机模型的遥感图像亚像元定位

由于遥感图像中普遍存在混合像元,因此传统分类方法得到的结果通常会存在较大误差,应用混合像元分解技术,虽然可以得到混合像元中各端元组分的丰度,但是却不能得到各端元组分的空间分布状态,而亚像元定位则是在混合像元分解的基础上,将混合像元剖分为亚像元,再利用端元组分的丰度及像元空间分布的特点,将亚像元赋予不同端元组分来得到各端元组分的空间分布情况,以提高遥感图像分类的精度。为了更好地解决亚像元定位问题,结合亚像元定位的理论模型,提出了一种新的元胞自动机模型,并通过模拟数据和实际数据对该模型进行了检验,结果表明,该模型是一种简单有效的解决亚像元定位问题的方法。     

基于正则化方法的遥感图像混合像元分解

由于传感器的分辨率的限制,在低空间分辨率遥感图像中存在着大量的混合像元.混合像元所表示的并不是单一地面物体类别的光谱反射值,而是多种类别的反射光谱的组合.混合像元的混合模型可以分为线性混合模型和非线性混合模型.线性混合模型是最常用的一种解混合方法,对于线性混合模型的求解算法进行了研究,根据最小二乘原理,提出了基于正则化方法的线性混合模型求解算法,对实际遥感TM图像进行了解混合运算,求得了端元丰度图像和伪彩色合成图像.     

遥感影像亚像元定位研究综述

遥感影像亚像元定位是在混合像元分解基础上,利用地物空间分布特征确定不同地物类型在混合像元中的具体位置,得到亚像元尺度的地物分类图,是一种有效解决混合像元空间不确定性的方法。首先介绍遥感影像亚像元定位的基本概念,分析亚像元定位的理论模型和求解算法;然后总结亚像元定位模型的误差来源、精度评价方法以及结果不确定性的表达手段,同时讨论利用辅助数据源提高亚像元定位精度的主要方法;最后对亚像元定位的研究趋势做了进一步展望。     

结合超分辨率重建的神经网络亚像元定位方法

遥感影像中普遍存在着混合像元,如何分析和解译混合像元一直是人们研究的热点。亚像元定位方法是将混合像元分解成为亚像元,并赋予不同的端元组分,以提高影像整体分类精度的一种技术。本文在神经网络亚像元定位模型的基础上,结合超分辨率重建理论,提出一种新型的BPMAP模型,在每一个类别的组成分图像与亚像元定位图像之间建立起高、低分辨率的观测模型,采用最大后验估计（MAP）算法对BP神经网络的定位结果进行约束,最终确定混合像元内部各组分合适的空间位置。通过对模拟的简单图像和长江三峡地区的ETM影像进行实验,结果表明,与神经网络模型相比,本文方法能够更加有效地解决亚像元定位的问题,进一步消除定位过程中产生的误差,提高精度。     

Sub-pixel Mapping with Partheno-Genetic Algorithm and Sub-pixel/pixel Spatial Attraction Model

Mixed pixels are widely presented in remotely sensed images.Soft classification techniques can avoid the loss of information comparing to hard classification methods while handling mixed pixels.However,the assignment to these classes by soft classification does not specify the location in the pixel.Sub-pixel mapping (or super-resolution mapping) is a technique which designed to use the information obtained by soft classification to get a sharpened image and it can incorporate benefits of both hard and soft classification techniques.In this paper,a variation of genetic algorithm,named as partheno-genetic algorithm (PGA),is developed to accomplish the sub\|pixel mapping.With the sub-pixel/pixel attraction model,PGA can achieve sub-pixel mapping in a straightforward one-pass process.It is evaluated with artificial and degraded land cover images by visual and quantitative classification accuracy indices.The results show this method can increase accuracy while compared to hard classification.     

基于局部连续性与全局相似性的光谱保持型亚像元映射

遥感图像的像元级分类精度受混合像元的影响. 亚像元映射以像元分解获得的丰度值为基础,在地物分布规律的约束下,细化估计各类地物的亚像元级分布模式. 本文同时考虑了地物分布的空间与光谱信息,提出了一种基于局部连续性与全局相似性的光谱保持型亚像元映射算法. 针对地物的空间分布特性,提出了利用类内离散度对局部连续性进行建模,并通过相似分布像元表示误差引入全局相似性约束项. 针对地物的光谱特性,采用最小化光谱误差约束了亚像元映射过程中的光谱无失真性. 模拟数据与真实数据上的实验结果表明,本文算法比其他同类算法具有更高的估计精度,且更适合于实际应用.     

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.