基于三维Saab变换的高光谱图像压缩方法

高光谱图像中存储了丰富的光谱信息,具有极大的应用价值,但现有大部分高光谱图像压缩方法难以同时兼顾图像中的空间冗余与谱间冗余,导致压缩性能受到局限。针对该问题,提出了一种基于三维修正偏置的子空间(Saab)变换的高光谱图像压缩方法。采用三维Saab变换对高光谱图像的分块进行空间光谱信息融合的降维操作,同时去除谱间冗余和局部空间冗余;利用高效率视频编码(HEVC)中的帧内编码模块进一步去除空间冗余和统计冗余;实现低失真、高比率的高光谱图像压缩。在多个高光谱图像数据集上的实验结果表明,所提方法在同码率下重建图像的信噪比(SNR)比采用主成分分析(PCA)降维的方法至少提高0.62 dB,在高码率的情况下性能优于张量分解的压缩方法。同时,验证了不同降维方法对分类任务的性能影响,结果表明,所提方法更好地保留了图像中的重要特征,在低码率的情况下仍可以保持较高的分类精度。      

基于多种空间信息的高光谱遥感图像分类方法

在高光谱遥感图像分类方法中,空间特征和光谱特征的融合可以有效地改善分类效果。针对单一空间特征的信息表达不充分问题,提出了一种联合多种空间特征的高光谱图像空谱分类方法。利用超像素信息对分类结果进行后处理去掉椒盐噪声,并创造性地将超像素信息应用于分类前处理,提出了一种利用超像素信息对像素点的特征向量进行线性加权融合的方法。试验结果表明,所提方法的性能优于目前的通常方法。     

Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

基于形态学与正交子空间投影的端元提取方法

针对现有端元提取方法从数据光谱或空间或特征信息的单一方面出发进行混合像元分解、不同类型端元难以区分等不足,提出一种扩展形态学与正交子空间投影结合的端元自动提取方法.利用扩展膨胀和腐蚀操作,通过计算形态离心率指数进行高光谱数据的端元数据集计算;利用光谱角匹配方法提取不同类型的端元,通过向端元正交子空间投影消除已经提取端元的影响;并通过航空高光谱数据进行算法性能验证.实验结果表明:提出方法能够实现在无任何先验信息情况下图像端元的自动提取,并且能够有效地区分相似光谱端元.     

基于抗噪声ICA的高光谱数据特征提取方法

特征提取是高光谱数据应用的一个重要环节,用于将高光谱数据中具有特殊性质的地物分离出来并去除冗余信息.提出了一种使用独立成分分析(ICA,Independent Component Analysis)进行高光谱遥感地物特征提取的方法.为了解决ICA对噪声过分敏感的问题,采用最大噪声分量(MNF,Maximum Noise Fraction)算法替代传统的主成分分析对数据作降噪处理,由MNF引出的不完全独立成分分析(UICA,Undercomplete ICA)在不牺牲特征提取能力的情况下能够获得很高的运算效率.给出了HYDICE和PHI的数据试验结果,分别测试了算法在时间效率和特征提取能力方面的性能,证明了该算法具有预期的性能.     

基于多重分形参数的高光谱数据特征提取

针对单一分形维数不能表征高光谱数据光谱局部吸收特征的问题,提出了基于光谱概率测度的多重分形参数特征提取方法.基于光谱信息度量进行光谱概率测度的计算,基于配分函数法估计得到尺度函数;通过对尺度函数求导计算出Holder指数,并对尺度函数勒让德Legendre变换计算出多重分形谱;从多重分形谱和Holder指数之间的函数关系提取表征多重分形谱形态的4个多重分形谱参数作为光谱特征参数;并应用于基于最小距离准则的航空推扫式高光谱成像仪(PHI,Prush-broom Hyperspectral Imager)图像监督分类.结果证明:利用基于光谱概率测度的多重分形参数特征提取方法提取的光谱特征参数进行分类得到的总体分类正确率达94.789%,分类精度明显高于利用信息量维数和多重分形谱特征提取方法进行分类的结果,证明了基于光谱概率测度的多重分形参数特征提取方法提取的多重分形参数的有效性和可靠性.     

改进独立成分分析在高光谱图像分类中的应用

针对独立成分分析在使用常规数值求解时容易陷入局部最优解的问题,以及采用神经学习算法时神经元激活函数的限制问题,将遗传算法与独立成分分析相结合,并对模型进行改进,提出了适合于高光谱数据无监督分类的模型.该算法采用最大化非高斯性进行成分的统计独立性度量,利用四阶累积量-峰度作为遗传算法的适应度函数.在应用分析中,将该算法应用于推扫式高光谱成像仪(PHI,Push-broom Hyperspectral technique Imager)数据地物分类能够获得全局最优解,在没有先验信息情况下实现地物的精细分类;与传统高光谱无监督分类算法比较,表明该算法的适用性,并具有更高的分类精度和准确性.      

Unmixing method for hyperspectral data based on sub-space method with learning process

An unmixing method for hyperspectral Earth observation satellite imagery data is proposed. It is based on a sub-space method with learning process. The proposed method utilizes a sub-space for feature space during unmixing. It is used to be done in a feature space which consists of spectral bands of observation vectors. As the results from the experiments with airborne based hyperspectral imagery data, AVIRIS, it is found that the proposed unmixing is superior to the other existing method in terms of decomposition accuracy and the process time required for the decompositions.     

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.     

基于PM算法的涡旋电磁波引信超分辨测向方法

为提高防空导弹无线电引信在低信噪比（SNR）背景下的测向精度，提出了基于传播算子算法（PM）的涡旋电磁波引信超分辨测向方法。通过相控均匀圆阵依次发射不同轨道角动量模态涡旋电磁波照射目标，基于多发单收（MISO）模型推导目标探测回波模型；通过对回波方程的近似变形构造接收信号矩阵；采用PM算法构造空间谱函数，进行谱峰搜索，实现对目标水平方位角的估计。对所提方法的有效性进行仿真验证，结果表明，所提方法可在低信噪比、少阵元情况下实现对目标的精确测向。      

基于记忆关联学习的小样本高光谱图像分类方法

高光谱图像（HSI）分类是遥感领域的基础应用之一。该任务旨在根据部分带类别标签的像素样本训练分类器，预测图像中剩余像素对应的类别标签。在实际应用中，由于人工标记样本成本过高，只能获得少量带标签的样本。针对少量样本无法准确描述数据分布从而导致训练过程过拟合的问题，提出一种基于记忆关联学习的小样本高光谱图像分类方法。考虑到无标签样本中包含大量与数据分布相关的信息，构建基于有标签样本记忆模块，并根据样本间的特征关联，利用不断更新的记忆模块学习无标签样本的潜在类别分布，构建无监督分类模型，并与传统的有监督分类模型进行联合学习。在多个高光谱图像分类数据集上的实验结果表明，所提方法能有效提升小样本高光谱图像分类的准确性。      

基于光谱知识的高光谱图像自动识别方法

针对传统高光谱图像矿物识别方法未能充分利用矿物光谱诊断吸收特征与矿物光谱知识、识别过程人为干预多等问题,提出了一种基于光谱知识的高光谱图像自动识别方法.该方法引入了基于光谱吸收特征与波形特征的光谱知识作为自动识别的标准,利用连续统去除操作增强光谱吸收特征,采取基于光谱主次吸收特征的识别决策策略,建立多级约束准则以提高识别精度及避免误识别,通过利用模拟数据进行算法精度评价并应用航空高光谱成像仪AVIRIS(Airborne Visible/Infrared Imaging Spectrometer)数据进行应用分析与验证.结果表明:当图像信噪比大于200时,识别准确率可以达到80.3%,能够得到良好的识别结果以及较高的精度,并实现了基于高光谱图像的矿物自动识别.     

高光谱成像技术的应用与发展

介绍了国外先进高光谱成像载荷发展情况和国内以高分五号载荷为代表的高光谱成像技术发展水平,凭借在光谱分辨率、空间分辨率、成像质量等方面取得的长足进步,广泛应用于资源勘探和环境监测等多个重要领域。分析了Offner结构和Dyson结构的推扫型成像仪的特点,提出未来凝视型成像方式的发展前景,并介绍了目前基于AOTF分光方法的中红外高光谱成像仪,实现了较好的光谱成像效果。     

An improved land cover classification using polarization signatures for PALSAR 2 data

Land cover classification in mixed land cover scenarios is challenging with PolSAR data. Polarimetric decomposition techniques are most popular methods for PolSAR data classification in recent times. These techniques focus on identification of dominant scattering phenomena and hence result in sub-optimal classification in mixed land cover scenarios. Alternatively, polarization signatures (PSs) are good illustrations of SAR target responses as they depict a detailed physical information from target backscatter. Researchers have successfully utilized SAR PSs for land cover (LC) classification. Some reports suggested utilizing correlation between observed PSs and standard target PSs as features for LC classification. This paper presents a study on improved utilization of PSs for optimal LC classification in mixed class scenarios. First, PS based SAR features are derived using fully polarimetric SAR data. The features represent a degree of similarity between observed and standard PSs. The derived features are termed as polarization signatures correlation features or PSCFs. The novel PSCFs are analyzed, evaluated and compared with decomposition based features for the purpose of LC classification. Classification performance indicators highlight potential of PSCFs for mixed LC classification problems. Therefore, further an adaptive and optimal LC class boundary estimation approach for LC classification is proposed and developed. Observed PSs and reference LC class PS statistics are used to build empirical models between classification performance indicators and LC class boundaries. The empirical models are optimized using the evolutionary genetic algorithm to maximize classification performance. A decision tree is constructed based on the optimal class boundaries to prepare LC classification. The proposed classification approach is compared with some recent popular classifiers and comparison suggests that the proposed approach provides satisfactory results for mixed LC classification scenarios.     

国外高光谱遥感载荷发展分析

高光谱遥感是当前遥感领域发展的前沿。首先介绍了高光谱遥感的成像原理、典型成像方式及主要特点等基础理论,之后从机载和星载两个方面,系统梳理了美国、加拿大、欧洲、日本等主要国家高光谱遥感载荷的发展现状,分析了相关载荷的主要性能指标(以空间分辨率、频谱分辨率等为代表),总结、归纳了高光谱遥感在民用商用、国防安全及行星探测三个领域中的应用情况,最后从提升分辨率、优化探测模式及实现小型化三个方面,总结了高光谱遥感载荷的发展趋势。     

成像光谱技术超谱图像分类研究现状与分析

超谱遥感图像由于其高光谱分辨率的特点正受到国外内的广泛关注，在解决了前期的辐射校正及定标等剖以及，超谱图像的推广应用是当务之急。超谱遥感图像的分类研究对农作物生长状况的监测、矿物的识别、海洋水色分析以及其他方面的许多应用都是很有价值的。文章概述了国内外在成像光谱技术方面的发展，并首次以像元的构成成分的.纯像元和混合像元的角度对分类方法进行了论述，对超谱图像分类技术研究现状的归纳和分析，旨在寻找突破点，加强此领域的研究力度。     

基于倒置残差注意力的无人机航拍图像小目标检测

针对无人机航拍图像背景复杂、小尺寸目标较多等问题,提出了一种基于倒置残差注意力的无人机航拍图像小目标检测算法。在主干网络部分嵌入倒置残差模块与倒置残差注意力模块,利用低维向高维的特征信息映射,获得丰富的小目标空间信息和深层语义信息,提升小目标的检测精度;在特征融合部分设计多尺度特征融合模块,融合浅层空间信息和深层语义信息,并生成4个不同感受野的检测头,提升模型对小尺寸目标的识别能力,减少小目标的漏检;设计马赛克混合数据增强方法,建立数据之间的线性关系,增加图像背景复杂度,提升算法的鲁棒性。在VisDrone数据集上的实验结果表明：所提模型的平均精度均值比DSHNet模型提升了1.2%,有效改善了无人机航拍图像小目标漏检、误检的问题。     

一种空间目标RCS序列的姿态异常检测方法

准确、快速判断空间目标姿态运动模式异常,对于空间目标监测具有重要意义。针对空间目标雷达散射截面 (Radar Cross Section,RCS)序列,提出一种基于小波包分解（Wavelet Packet Decomposition,WPD）能量谱特征的无监督机器学习异常检测方法,并采用单类支持向量机(One Class Support Vector Machine,OCSVM)验证异常检测效果。设置了几种典型异常场景进行仿真分析,试验结果表明,该方法能有效检测出三轴稳定类空间目标发生失稳旋转的姿态异常。相比于传统统计参数特征、小波变换统计参数特征及能量特征的姿态判别方法,具有检测概率高、鲁棒性好的特点。     

Mineral mapping in the Kap Simpson complex,central East Greenland,using HyMap and ASTER remote sensing data

This research focuses on the application of HyMap airborne hyperspectral data and ASTER satellite multispectral data to mineral exploration and lithologic mapping in the Arctic regions of central East Greenland. The study area is the Kap Simpson complex in central East Greenland. The Kap Simpson complex is one of the largest exposed Palaeogene felsic complexes of East Greenland. It has been the target of several mineral exploration projects. The analysis of the HyMap data produced a detailed picture of the spatial distribution of the alteration minerals in the Kap Simpson complex, unavailable from field-based studies in the area. The analysis of the ASTER data produced mineral maps which due to the moderate spatial and spectral resolution of the ASTER imagery can be useful for reconnaissance level mineral exploration. Colour composites of the mean normalized ASTER thermal bands display lithological information and detected a large felsic igneous intrusion that has not been shown on the recently compiled geological maps of the area. The results of this research have considerable potential to evaluate the use of hyperspectral and multispectral remote sensing for geological purposes in the Arctic regions of central East Greenland.