多光谱遥感影像亚像元定位的空间引力算法研究

针对遥感影像亚像元定位问题,提出一种基于像元空间引力模型的亚像元定位新算法,算法中像元空间引力的表达在亚像元尺度上建立,能够表达像元间的空间自相关性;亚像元权重参数包括相互吸引的两个相邻像元中地物百分比含量,强化了空间引力模型;用距离函数表达像元间的相互作用在距离上的非线性关系.通过迭代运算优化像元间的引力关系,提高像...     

基于模糊ARTMAP神经网络模型的遥感影像亚像元定位

结合亚像元的相关理论.提出了基于模糊ARTMAP神经网络模型的遥感影像亚像元定位方法,利用该方法对模拟的武汉地区的TM影像进行了实验,并将实验结果与BP神经网络模型进行了比较.结果证明,利用本文方法能够更有效地解决亚像元定位的问题.     

MODIS数据的洪水淹没亚像元制图研究

针对MODIS影像中普遍存在的混合像元问题,该文基于MYD09GA地表反射率数据,实现了洪水淹没的亚像元级别的空间细节定位,并加入数字高程模型(DEM)高程信息修正了亚像元制图结果,两结果与传统硬分类法提取结果进行了比较。结果表明,亚像元制图算法较传统硬分类法对洪水淹没范围提取精度更高,能够很好地保持水体空间细节特征,DEM修正后的亚像元定位结果精度进一步提高。     

基于SVM的高光谱遥感图像亚像元定位

提出了基于支持向量机（support vector machine,SVM）的高光谱遥感图像亚像元定位方法。全变分（total variation,TV）模型是经典的保边缘平滑滤波器,本文将其引入作为预处理,来提高混合像元分解及亚像元定位的精度;本文方法在训练和检验样本的构建过程中,依据空间相关性理论,同时考虑了中心像元及其邻近像元丰度值对亚像元类别归属的影响;在监督分类训练和检验过程中,通过剔除纯净像元来缩减样本数量,在保证算法准确性的同时提高了效率。对真实高光谱遥感数据进行了实验,主观评价和定量分析验证了本文方法的有效性。     

基于空间自相关BP神经网络的遥感影像亚像元定位

亚像元定位技术是一种获取地物在混合像元中分布信息的有效方法.提出一种基于空间自相关函数的遥感影像BP神经网络亚像元定位方法,与传统的BP神经网路亚像元定位方法相比,该方法利用空间自相关函数Moran's I 在亚像素级上对定位结果进行约束,其结果更符合空间相关性假设理论.试验结果表明,该方法优于传统BP神经网络亚像元定...     

高光谱遥感图像亚像元信息提取方法综述

高光谱遥感图像光谱分辨率高、波谱连续、图谱合一,这为精细地物分类、探测和识别提供了数据基础。然而,由于高光谱遥感图像空间分辨率的局限性及地物场景的复杂分布,混合像元普遍存在于高光谱遥感图像。混合像元是高光谱遥感图像精细信息提取与分析中的难点。解决混合像元问题,实现亚像元级信息的提取与分析是近年来高光谱遥感图像解译的热点和前沿。本文系统梳理了高光谱遥感图像亚像元信息提取的主要研究内容,具体从混合像元分解、亚像元制图及亚像元目标探测3个研究方向综述了经典方法,并对国内外相关方向的研究进展、发展前沿及主要挑战进行了分析与评价,最后分析讨论了高光谱遥感图像亚像元信息提取研究在模型构建、优化求解及与应用结合等方面的研究趋势及方向。     

高光谱亚像元定位的线特征探测法

未考虑地物亚像元级空间结构特征是影响高光谱亚像元定位精度的因素之一。为了有效解决这一问题,本文提出一种基于混合像元线特征探测的亚像元定位算法。首先,通过光谱解混确定含典型线状地物的混合像元。然后,基于完备直线集的最大线性指数方法确定其余含线特征的混合像元,使用模板匹配方法结合像元引力确定含线特征混合像元的亚像元类别。最后,基于线性优化的方法迭代确定剩余混合像元的亚像元类别。通过真实数据及仿真数据的试验,结果表明所提出的方法能有效提高亚像元定位精度。     

一种基于进化Agent的遥感影像亚像元定位方法

遥感影像中存在着昆合像元,软分类技术将这些像元按照一定的百分比划分为不同的地物类别,亚像元定位技术利用在每个混合像元中所获得的百分比信息,得到一个锐化后的分类影像.像元分解成不同的子像元,代表不同的地物类别成分.进化Agent技术结合一种空间邻域的假设关系,通过繁殖和扩散两种行为模式,分配给每一个亚像元一个确定的位置,从而达到定位的效果.利用合成影像和退化的真实影像进行实验,通过与传统的硬分类进行精度比较,证明进化Agent技术是一种简单易行的亚像元定位算法.     

基于模拟退火算法的遥感影像亚像元定位方法

根据地物空间分布自相关原理,提出一种基于模拟退火算法的亚像元定位新方法,通过优化子像元的空间分布,最终确定混合像元中各组分的位置。通过遥感影像数据对模型进行测试,并使用已有评价指标对试验结果的视觉效果和分类精度进行评价,试验证明本方法能更好地重建地物空间分布,与硬分类方法相比能显著提高分类精度。     

混合像元分解技术及其进展

混合像元分解模型是定量遥感研究的重要组成部分,为各种地学应用提供了更精细的亚像元级地物信息,这一领域受到国内外学者们广泛关注。本文围绕混合像元分解研究的4个核心问题——光谱混合模型、端元提取、模型反演方法以及解混精度评估,总结了近20年来混合像元分解的重要研究进展,分析和介绍了典型算法模型的原理和思路。进一步阐述了现有研究在一些关键问题上存在的不足,如目前仍缺乏公认的线性和非线性模型的选择判据、已有的混合像元分解模型无法抑制由端元光谱相关造成的共线性问题。最后总结了混合像元分解未来的发展趋势和值得探索的研究方向。如结合辐射传输模型和地面试验,定量分析多次散射的影响机制,以及结合克服共线性的统计回归模型。     

Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery

Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient constraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images.     

超像元尺度下的多光谱图像超分辨率洪水淹没制图

超分辨率制图SRM (Super-resolution Mapping)技术可以有效地处理遥感图像中的混合像元,获得准确的地物类别分布信息。目前,SRM技术已经成功地应用于多光谱图像洪水淹没定位中,称为超分辨率洪水淹没制图SRFIM (Super-resolution Flood Inundation Mapping)。然而,现有的SRFIM方法往往基于像元尺度空间相关性,这种空间相关性考虑设定的矩形窗内的像元之间的空间关系,但实际情况下淹没区域与非淹没区域的形状是不规则的,因此这种像元尺度空间相关性不够准确,影响最终的洪水淹没制图精度。为了解决这一问题,提出了超像元尺度空间相关性下的多光谱图像超分辨率洪水淹没制图SSSC-SRFIM (Super-resolution Flood Inundation Mapping for Multispectral Image Based on Super-pixel Scale Spatial Correlation)。在SSSC-SRFIM中,首先利用双立方插值改善原始粗糙多光谱图像,获得改善后的图像,并利用光谱解混方法对改善后的图像进行光谱解混,获得具有每个亚像元属于淹没类别概率值的丰度图像;然后利用主成分分析法提取改善后图像的第一主成分,并利用基于多分辨率的图像分割算法分割第一主成分,获得不规则形状的超像元;再者将丰度图像与超像元进行整合计算,并引入随机游走算法计算各个超像元之间的空间相关性;最后,依据超像元空间相关性,利用基于类别单元的类别方法将淹没区域或非淹没区域标签分配给每个亚像元中,得到最终的洪水淹没制图结果。利用两个Landsat 8 OLI多光谱图像对该方法进行了评价。结果表明,与传统的SRFIM方法相比,本文提出的SSSC-SRFIM方法具有更好的效果。     

Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation

Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images.     

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.     

Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.     

地籍时空数据模型与宗地变更

目前多数地籍信息系统尚未开发出完善的宗地变更和管理功能;针对该问题,本文提出适合宗地变更的地籍时空数据模型。探讨了把时空对象映射到关系型数据库中去的方法,并研究了宗地信息变更法。相关的实践结果表明了该模型和映射方法的有效性,建立的地籍信息系统能在地籍日常管理中发挥应有的作用。     

提高中巴卫星IR MSS图像空间分辨能力的光谱保真融合方法

介绍一种提高中巴资源卫星IRMSS图像空间分辨能力的光谱保真融合方法。通过计算低分辨率图像上每一个像元对应的高分辨率图像上一组子像元的平均亮度值及二者之差,将该差值与高分辨率图像上相应子像元亮度求和,形成新的图像。该图像具有高分辨率图像的空间细节,又具有低分辨率图像的光谱信息,从而实现融合图像信息保真。试验表明,光谱保真融合方法可以在不改变光谱信息的前提下提高IRMSS图像的空间分辨能力,是一种新的简单实用的数据处理方法。