基于正则MAP模型的遥感影像亚像元定位

为了更好地解决亚像元的定位问题，基于超分辨率影像重建的技术，结合亚像元定位理论，提出了一种应用于亚像元定位的正则MAP估计模型，并且通过真实数据进行了检验。实验表明，该模型是一种简单、有效地解决亚像元定位问题的方法。     

基于MAP的高光谱影像增强方法

介绍了MAP方法的基本原理,建立了适用于高光谱影像增强的观测模型,给出了模型的代价函数.在假设高光谱影像像点相互独立的条件下,给出了代价函数的求解方法,分析并给出了基于MAP的高光谱影像增强流程.利用PHI高光谱影像及其对应的高分辨率全色影像数据,进行了MAP高光谱影像增强实验.实验结果的定量分析表明,本文方法能够在保...     

矢量C-V模型的高光谱遥感影像分割

高光谱遥感影像除了包含普通2维影像所具有的空间信息还包含了1维光谱信息,传统的针对2维影像的分割方法不能很好地应用于高光谱遥感影像。为此,本文提出一种能够同时处理多波段影像的高光谱遥感影像矢量C-V模型分割方法。首先选出高光谱遥感影像中目标与背景对比度较大的波段,并通过计算波段相关系数,去除其中的冗余信息形成新的波段组合,进而根据所确定的波段组合构建高光谱遥感影像矢量矩阵;在此基础上,构造基于该矢量矩阵的矢量C-V分割模型。模型中通过引入基于梯度的边缘引导函数,在保留传统C-V模型基于区域信息进行影像分割的基础上,利用影像的边缘细节信息,增强了模型在异质区域和复杂背景情况下对目标边缘的捕捉能力,提高了对高光谱遥感影像的分割精度和速度。最后利用HYPERION数据进行仿真实验,并将实验结果和传统C-V模型和相关方法进行了对比,结果表明,本文方法能够在短时间内有效地分割高光谱遥感影像,与传统方法相比,具有分割精度更高运算速度更快的特点。     

Object-based classification of hyperspectral data using Random Forest algorithm

This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI data-sets, respectively. Moreover, this method showed better efficiency in comparison to the spectral-based classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.     

空间与谱间相关性分析的NMF高光谱解混

非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。     

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.     

天宫一号高光谱数据辐射校正的CUDA并行优化

高光谱图像经过辐射校正后,消除了探测元的响应差异,能更好地满足专题信息提取的数据要求.利用探测元的列均值、列标准差等统计信息对天宫一号高光谱短波红外数据进行辐射校正检验,并基于GPU CUDA计算模型对均值归一化、矩匹配、相邻列均衡等3种相对辐射校正算法进行了并行计算优化.通过辐射校正计算流程拆分,CPU控制流程逻辑,GPU执行数据级并行计算,并建立CUDA的计算单元与数据单元的映射关系,获得5—7倍的计算加速比,这些辐射校正算法依据图像自身统计信息,且易于进行并行计算优化,满足实时校正的处理时效要求,为未来高光谱数据在轨实时辐射校正提供了新思路.     

联合空-谱信息的高光谱影像深度三维卷积网络分类

针对高光谱影像分类高维和小样本的特点,提出一种基于深度三维卷积神经网络的高光谱影像分类方法。首先,该方法直接以高光谱数据立方体为输入,利用三维卷积操作提取高光谱数据立方体的三维空-谱特征。然后,利用残差学习构建深层网络,提取更高层次的特征表达,以提高分类精度。最后,采用Dropout正则化方法防止过拟合。利用Pavia大学、Indian Pines和Salinas 3组高光谱数据进行试验验证,结果表明,与支持向量机和现有的基于深度学习的高光谱影像分类方法相比,该方法能有效提高高光谱影像的地物分类精度。     

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.     

一种基于多源遥感影像的亚端元光谱提取方法

提出一种利用多源影像来提取低分辨影像亚端元光谱的方法,该方法利用高分辨分类得到的组分与低分辨的光谱之间的线性混合关系,建立了反演出低分辨影像的亚端元光谱的线性模型,并提出一种快速鲁棒反演亚端元光谱的提取方法。利用IKONOS和ETM数据验证了本文提出方法,实验结果表明了该方法的有效性。     

高分五号高光谱图像自编码网络非线性解混

针对高光谱非线性混合模型中的共线性问题,提出了一种非监督的增强型非线性自编码网络方法 ENAE(Enhanced Nonlinear Autoencoder)。通过结合自编码网络在挖掘数据内在结构、提取特征方面的优势,引入端元正则项减弱端元间的共线性效应,从而提高高光谱混合像元分解精度。ENAE方法的实现步骤主要包括两部分:一是网络结构初始化,二是非线性分解。网络结构初始化是确定编码器的节点数以及端元和丰度的初值;非线性分解则主要是实现损失函数的最小化。通过模拟数据、城市区域真实数据和高分五号卫星高光谱数据的实验,得到了相较于传统非线性分解方法更高的精度,证明了ENAE方法的鲁棒性。     

波段选择协同表达高光谱异常探测算法

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。     

不同降维策略下的高光谱影像多特征分类

由于物体表面的空间分布通常是富有规律且局部连续的,在高光谱影像分类中应充分利用其光谱和空间信息。本文在对高光谱影像立方体进行降维处理的基础上,提出了一种联合空域和谱域信息的高光谱影像高效分类方法。首先,分别选用主成分分析(Principal Component Analysis,PCA)和正交投影波段选择(Orthogonal Projection Band Selection,OPBS)两种方法对原始高光谱数据进行预处理,获取降维后的影像数据。然后在其基础上提取扩展形态学特征(Extended Morphology Profiles,EMP)和地物表面纹理特征,组成联合光谱和纹理、形状结构特征。最后,采用支持向量机(Support Vector Machine,SVM)分类器对联合特征进行分类。针对不同真实高光谱数据集的实验结果表明,本文提出的方法运算效率高且具有令人满意的分类性能。     

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。     

空-谱融合的条件随机场高光谱影像分类方法

高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免“同谱异物”与“同物异谱”的现象方面有较明显的优势。     

高光谱数据截断加权核范数稀疏解混

受仪器和观测条件限制,高光谱数据易受噪声污染,给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题,本文提出一种截断加权核范数稀疏解混方法,利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势,在稀疏解混中加入基于截断加权核范数的低秩约束,并结合加权稀疏技术,在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理,可以更好地实现丰度矩阵的低秩逼近,使丰度图像保持空间一致性并保留更多细节信息,空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明,与其他先进的稀疏解混方法相比,所提方法具有更好的抗噪性,能够提高解混精度。     

基于多特征融合与典型降维方法的高光谱影像分类

高光谱影像的冗余信息给影像的分类效果带来一定的负面影响。本文利用CB法(CfsSubsetEval评估器结合Best-First搜索策略)与PCA变换两种降维方法,分别结合随机森林分类器对4种多特征融合方案(共8种组合)进行高光谱影像分类对比,基于分类的总体精度、Kappa系数探究提高高光谱影像分类的最佳组合方法。结果表明:①多特征融合可提升高光谱影像的分类效果,两种降维方法的分类精度均随地理特征、纹理特征、指数特征的加入而逐渐提高。②两种降维方法中,经CB法降维后的分类精度均比通过PCA变换降维的分类精度高。在构造的8种组合中,基于所有特征信息(光谱特征、地理特征、纹理特征、指数特征)的CB法分类精度最高,其总体精度为98.01%;Kappa系数为0.969 9。     

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。     

利用能量变分方法进行高光谱数据去噪处理

利用超平面最小方案,针对高光谱数据在空间维和光谱维建立能量函数,通过两个权重系数调节空间维数据曲面光滑程度和光谱曲线光滑程度,达到联合抑制噪声的目的。实验中,对Hamamatsu相机和AVIRIS采集的高光谱影像数据中比较严重的噪声污染,该方法有效地降低了噪声的影响,在AVIRIS水吸收带处的去噪效果尤为明显。     

Detail focused fusion of hyperspectral and multispectral imagesEI北大核心CSCD

Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas. The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient. Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics. This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail. © 2022 National Remote Sensing Bulletin. All rights reserved.