利用热红外多光谱遥感技术提取和识别岩石、矿物信息

利用热红外多光谱遥感技术提取和识别岩石、矿物信息，是近年来遥感地质研究中的热点之一。在介绍岩石、矿物的热红外发射特性的基础上，着重阐述了热红外多光谱遥感技术在提取和识别岩石、矿物信息方面的应用，并对这一研究领域今后的发展方向提出了展望。     

地中海生态系统中土地退化,土壤侵蚀和沙漠化遥感监测

简要介绍了国外应用遥感图像进行土地退化制图和监测的一种新方法。遥感图像上像元亮度值代表土地表面多种物质（土壤、植被、阴影、母岩）混合光谱辐射特征，通过多光谱图像辐射校正和典型区野外光谱测试，利用线性光谱混合模拟可以将ＡＶＩＲＩＳ和ＴＭ图像混合光谱中差异明显的几个组分进行分离，进而在各组分丰度分析的基础上，比较准确地进行植被盖度、土壤条件与土壤侵蚀的判别与制图。     

热红外与多光谱遥感图像的神经网络回归融合方法研究

为验证神经网络方法用于遥感图像融合的有效性,归纳了利用神经网络对遥感数据进行回归来实现融合的3种途径,并提出了一种结合图像数据回归和多光谱遥感图像锐化技术来实现热红外图像的全色锐化新方法。这种热红外图像的全色锐化方法,利用了极限学习机（ELM）这种新型神经网络算法,快速高效地由训练样本得到遥感图像数据间的回归关系;同时,方法注重图像数据本身的物理含义,以提高热红外图像数据的真实质量为目标,是一种定量化的图像融合方法。经这种方法融合得到的热红外数据也能很好地用于定量遥感的物理模型,为遥感的实际应用提供方便。该方法的有效性通过对ETM+图像进行实验得到了证明,而直接对热红外图像数据和全色图像数据进行回归的融合模式,在实验中则无法得到满意的结果。     

天空地一体化热红外遥感图像仿真算法研究

遥感图像仿真对航天传感器前期技术论证以及遥感应用模型算法开发起到了重要作用。针对国内外热红外对地观测计划蓬勃发展的同时对热红外遥感模拟图像提出了迫切需求,提出了天空地一体化热红外遥感图像仿真算法。采用集地面场景模拟、大气辐射模拟、遥感器模拟于一体的全链路热红外遥感图像仿真模拟方法,实现了星载推扫式热红外遥感图像仿真;设计并开发了天地一体化热红外遥感图像仿真系统,同时对模拟出的热红外遥感图像进行了几何和辐射方面的评价和分析。实验效果表明,该算法可为热红外遥感器的性能评价与数据预处理算法预研究提供强大模拟数据支撑。     

基于结构性字典学习的毛儿盖遥感图像去噪研究

遥感图像的噪声分析、评估和滤波作为遥感图像处理的研究重点而一直受到遥感应用领域的关注。为了进一步提高遥感图像的去噪能力，提出一种新的基于聚类的组稀疏字典学习多光谱遥感图像去噪算法，该算法能够综合利用多光谱遥感图像的空间局部性和光谱的全局性，对遥感图像像素进行聚类后划分为不同的组，然后通过字典学习获得多光谱遥感图像的空间、光谱字典和系数。经过阈值处理后，对空间相似的块进行平均处理，实现了对多光谱遥感图像的去噪。该算法用于岷江上游植被和土壤类型典型地区——毛儿盖实验区遥感图像的去噪，峰值信噪比相比band-wise K-SVD算法提高了7.6%左右，同时具有更好的视觉效果。     

TM和SAR遥感图像的不同层次融合分类比较

多传感器遥感图像为分类技术提供了更多的地物特征信息,有助于提高分类精度,增强计算机自动解译的能力,减少遥感图像的后处理时间。给出了ＴＭ 和ＳＡＲ遥感图像根据信息处理所在的不同层次融合分类特征的选取及分类方法,并从融合分类的精度和计算时间消耗上对哈尔滨市附近地区５１２×５１２ 大小的ＴＭ和ＪＥＲＳ１ 的ＳＡＲ图像的不同层次融合的分类进行比较,指出特征层融合是ＴＭ和ＳＡＲ遥感图像融合分类的最理想层次     

基于结构性字典学习的毛儿盖遥感图像去噪研究

遥感图像的噪声分析、评估和滤波作为遥感图像处理的研究重点而一直受到遥感应用领域的关注。为了进一步提高遥感图像的去噪能力,提出一种新的基于聚类的组稀疏字典学习多光谱遥感图像去噪算法,该算法能够综合利用多光谱遥感图像的空间局部性和光谱的全局性,对遥感图像像素进行聚类后划分为不同的组,然后通过字典学习获得多光谱遥感图像的空间、光谱字典和系数。经过阈值处理后,对空间相似的块进行平均处理,实现了对多光谱遥感图像的去噪。该算法用于岷江上游植被和土壤类型典型地区——毛儿盖实验区遥感图像的去噪,峰值信噪比相比band-wise K-SVD算法提高了7.6%左右,同时具有更好的视觉效果。     

基于结构性字典学习的毛儿盖遥感图像去噪研究

遥感图像的噪声分析、评估和滤波作为遥感图像处理的研究重点而一直受到遥感应用领域的关注。为了进一步提高遥感图像的去噪能力,提出一种新的基于聚类的组稀疏字典学习多光谱遥感图像去噪算法,该算法能够综合利用多光谱遥感图像的空间局部性和光谱的全局性,对遥感图像像素进行聚类后划分为不同的组,然后通过字典学习获得多光谱遥感图像的空间、光谱字典和系数。经过阈值处理后,对空间相似的块进行平均处理,实现了对多光谱遥感图像的去噪。该算法用于岷江上游植被和土壤类型典型地区——毛儿盖实验区遥感图像的去噪,峰值信噪比相比band-wise K-SVD算法提高了7.6%左右,同时具有更好的视觉效果。     

独立分量分析在多光谱遥感图像分类中的应用

多光谱遥感图像反映了不同地物的光谱特征,其分类是遥感应用的基础。但是在多光谱遥感波段图像中存在不同地物对应着相同的灰度,即异物同谱的问题。独立分量分析算法对未知的源信号的混合信号进行估计,可以获得相互独立的源信号的近似。独立分量分析算法利用了信号的高阶统计信息,对于多光谱遥感图像而言,算法去除了波段图像之间的相关性,获得的波段图像是相互独立的。但是独立分量分析算法有一个缺点,即计算量太大,影响了在多光谱遥感图像分类上的应用。文章对独立分量分析的一种快速算法FastICA进行改进,减少了计算量,提高了算法的有效性。在性能相当的情况下,改进FastICA算法能有效地减少算法的计算量。由于FastICA算法是线性ICA算法,对于非线性混合的光谱信号的估计存在一定误差,因此应用BP神经网络的非线性特性对其进行自动分类。在同原始遥感图像的BP神经网络分类结果进行比较,结果表明独立分量分析算法能提高多光谱遥感图像的分类的正确率。     

多源遥感图像配准技术综述

从成像光谱特性、成像分辨率和成像模式等方面对可见光、红外、高光谱和合成孔径雷达传感器的成像特点进行分析,根据一致性特征描述方法对多源遥感图像配准算法进行分类,指出多源遥感图像具有成像特性变化大、相关度小、匹配特征的空间分布不均匀等特点,其配准技术的关键在于提取不变的图像特征以及得到有效的匹配特征。     

A comparison of thermal infrared emissivity spectra measured in situ,in the laboratory,and derived from thermal infrared multispectral scanner (TIMS) data in Cuprite,Nevada, U.S.A.

In order to obtain ground truth data for multispectral thermal infrared sensors such as TIMS and ASTER, in situ spectral emissivity measurements were made during field surveys. These spectral emissivity measurements and laboratory spectral reflectance measurements of field samples were compared to emissivity spectra extracted from TIMS data at the surveyed points. The results indicate that emissivity spectra derived from the TIMS data agree well in shape with the spectra measured in situ or in the laboratory.     

Study of land cover classification based on knowledge rules using high-resolution remote sensing images

This paper deals with the limitations of visual interpretation of high-resolution remote sensing images and of automatic computer classification completely dependent on spectral data. A knowledge-rule method is proposed, based on spectral features, texture features obtained from the gray-level co-occurrence matrix, and shape features. QuickBird remote sensing data were used for an experimental study of land-use classification in the combination zone between urban and suburban areas in Beijing. The results show that the deficiencies of methods where only spectral data are used for classification can be eliminated, the problem of similar spectra in multispectral images can be effectively solved for the classification of ground objects, and relatively high classification accuracy can be reached.     

Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data

Recently, the development of various remote sensing sensors has provided more reliable information and data for identification of different ground classes. Accordingly, multisensory fusion techniques are applied to enhance the process of information extraction from complementary airborne and spaceborne remote sensing data. Most of previous research in the literature has focused on the extraction of shallow features from a specific sensor and on classification of the resulted feature space using decision fusion systems. In recent years, Deep Learning (DL) algorithms have drawn a lot of attention in the machine learning area and have had different remote sensing applications, especially on data fusion. This study presents two different feature-learning strategies for the fusion of hyperspectral thermal infrared (HTIR) and visible remote sensing data. First, a Deep Convolutional Neural Network (DCNN)-Support Vector Machine (SVM) was utilized on the features of two datasets to provide the class labels. To validate the results with other learning strategies, a shallow feature model was used, as well. This model was based on feature fusion and decision fusion that classified and fused the two datasets. A co-registered thermal infrared hyperspectral (HTIR) and Fine Resolution Visible (Vis) RGB imagery was available from Quebec of Canada to examine the effectiveness of the proposed method. Experimental results showed that, except for the computational time, the proposed deep learning model outperformed shallow feature-based strategies in the classification performance that was based on its accuracy.     

无人机可见光遥感影像地物目标提取技术研究

无人机可见光遥感影像中地物目标边界清晰度较低,容易导致地物目标与背景之间的区分度降低,进而难以提取地物目标。为此,提出无人机可见光遥感影像地物目标提取方法。从光谱特征、纹理特征和边缘特征三个方面分析无人机可见光遥感影像特征。结合三种影像特征对无人机可见光遥感影像数据集实行增广处理。对完成增广后的数据集定义影像编码标签,以此确定地物目标增强权重,通过参量化处理地物目标光谱特征,计算光谱吸收指数,获取地物目标提取表达式,从而实现无人机可见光遥感影像地物目标提取。实验结果表明,所提方法能够保证地物目标边界的清晰度,具有较强的地物目标提取能力。     

高分辨率遥感卫星影像的河流提取方法研究

为了克服单纯采用光谱信息提取河流的缺陷,利用高分辨率遥感影像突出的高分辨率的特性提出一种综合影像中光谱、纹理、几何特性等多特征联合提取河流的方法。该方法分别对河流水体的光谱特征、纹理特征及河流几何形状进行描述,选取特征参数,构造综合特征矩阵,利用均值聚类分割最终得到河流目标。通过对真实高分辨率遥感影像Worldview1影像进行的实验验证了该方法的高精准性及快速性。     

线性约束与局部对比相耦合的多光谱遥感图像中目标探测

目标探测是遥感影像信息提取中的重要内容,然而,随着目标像元数目增多和相似地物的干扰,目标探测的虚警率会明显上升。将线性约束最小方差方法(LCMV)与局部对比方法(LCM)相结合,构建了一种新的多光谱遥感图像中目标探测方法(LCLCM):首先利用样本相关矩阵对目标进行半解混,然后利用图像的空间性增强目标信息、抑制背景信息,最后进行图像归一化和图像分割。以Landsat 8多光谱图像中船只提取为例进行方法验证,LCLCM的虚警率为1.07%,优于LCMV和LCM的虚警率12.39%和11.26%,表明该方法能够进行有效稳健的目标探测。     

Estimating life-form cover fractions in California sage scrub communities using multispectral remote sensing

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems is known for its high biodiversity and is home to a large number of rare, threatened, and endangered species. Because of extensive urban development in the past fifty years, this ecologically significant community type is highly degraded and fragmented. To conserve endangered CSS communities, monitoring internal conditions of communities is as crucial as monitoring distributions of the community type in the region. Vegetation type mapping and field sampling of individual plants provide ecologically meaningful information about CSS communities such as spatial distribution and species compositions, respectively. However, both approaches only provide spatially comprehensive information but no information about internal conditions or vice versa. Therefore, there is a need for monitoring variables which fill the information gap between vegetation type maps and field-based data. A number of field-based studies indicate that life-form fractional cover is an effective indicator of CSS community health and habitat quality for CSS-obligated species. This study investigates the effectiveness of remote sensing approaches for estimating fractional cover of true shrub, subshrub, herb, and bare ground in CSS communities of southern California. Combinations of four types of multispectral imagery ranging from 0.15 m resolution scanned color infrared aerial photography to 10 m resolution SPOT 5 multispectral imagery and three image processing models - per-pixel, object-based, and spectral mixture models - were tested.An object-based image analysis (OBIA) routine consistently yielded higher accuracy than other image processing methods for estimating all cover types. Life-form cover was reliably predicted, with error magnitudes as low as 2%. Subshrub and herb cover types required finer spatial resolution imagery for more accurate predictions than true shrub and bare ground types. Positioning of sampling grids had a substantial impact on the reliability of accuracy assessment, particularly for cover estimates predicted using multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery. Of the approaches tested in this study, OBIA using pansharpened QuickBird imagery is one of the most promising approaches because of its high accuracy and processing efficiency and should be tested for more heterogeneous CSS landscapes. MESMA applied to SPOT imagery should also be examined for effectiveness in estimating factional cover over more extensive habitat areas because of its low data cost and potential for conducting retrospective studies of vegetation community conditions.     

Thermal infrared spectra and images of altered volcanic rocks in the Virginia Range,Nevada

Abstract

Airborne Thermal Infrared Multispectral Scanner (TIMS) data and laboratory thermal infrared reflectance spectra were collected from sites of hydrothermally altered andesitic volcanic rocks near Virginia City, Nevada. Alunitic, kaolinitic, illitic, and propylitic alteration types have distinct laboratory spectral curves, although the individual spectra are difficult to interpret mineralogically. TIMS emittance spectra show a general shift in the wavelength of the silicate emittance minima to shorter wavelengths with increasing intensity of alteration, owing to the increasing abundance of secondary framework and sheet silicate minerals. Strongly altered volcanic rocksare identifiable on TIMS radiance and emittance imagery.     

基于地物反射特性的多波段遥感图像无损压缩方法

要实现多波段遥感图像的无损压缩,去除空间和谱间的相关性是其中的重要环节。通过分类,分析不同地物在不同波段的反射特性,再根据不同地物的波段反射特性构造出针对不同地物的谱间预测器,用以去除谱间相关性。对于去除谱间相关的残差图像,采用Ｓ＋ Ｐ变换,以去除空间相关。实验取得了令人满意的效果,证明了这种方法的有效性。