Ocean Observation from Haiyang Satellites: 2012–2014

During 2012 and 2014, China has two Haiyang(which means ocean in Chinese, referred to as HY) satellites operating normally in space which are HY-1B and HY-2A. HY-1B is an ocean color environment satellite which was launched in April 2007 to observe global ocean color and sea surface temperature, and HY-2A is an ocean dynamic environment satellite which was launched in August 2011 to obtain global marine dynamic environment parameters including sea surface height,significant wave height, ocean wind vectors, etc. Ocean observation data provided by HY-1B and HY-2A have been widely used by both domestic and international users in extensive areas such as ocean environment protection, ocean disaster prevention and reduction, marine environment forecast,ocean resource development and management, ocean investigations and scientific researches, etc.     

基于支持向量机的无参考遥感图像质量评价方法

针对遥感图像主观评价方法的低效率以及常用客观评价方法无法充分考虑人眼对图像的感知特性的问题,文章提出了一种基于支持向量机的无参考遥感图像质量（quality）评价方法。首先建立遥感图像主观评价库,然后在不需要图像失真信息的基础上,利用支持向量机（SVM）将图像的失真类型分为三类,并对每类进行单项评价,再通过加权得到遥感图像的总评分,最后将本文方法、信噪比与信息熵的评价结果回归到主观评价空间并进行对比。实验证明,文章所提方法能客观地评价遥感图像的质量,且优于信噪比和信息熵两种质量评价方法,其结果与人眼视觉感受相符。     

基于层次化分类器的遥感图像飞机目标检测

在大量航空航天遥感图像中,快速发现和统计飞机目标并对其进行准确定位,在军事和民用方面均具有重要意义。结合遥感图像特点,针对飞机目标的特征,文章设计了一种基于层次化的分类器的遥感图像飞机目标检测方法。首先用基于哈尔（Haar）特征的底层AdaBoost分类器快速去除大部分非目标区域;然后用基于梯度方向直方图（Histogram of Oriented Gradient,HOG）特征的顶层支持向量机（Support Vector Machine,SVM）分类器进行精细检测。在分辨率为1m的遥感图像数据集上的实验结果表明,层次化分类器在保证较高检测率的前提下,大大降低了虚警率,可以有效解决遥感图像飞机检测问题。     

中国无地面控制点摄影测量卫星追述（一）——返回式摄影测量卫星

以无地面控制点条件下的摄影测量为背景,回顾了中国第一代、第二代返回式摄影测量卫星的研制历程,给出了相机的主要技术参数,介绍了研制的技术难点。从地形图制作精度的角度评价了中国第一代、第二代返回式摄影测量卫星的技术水平,给出了制作的立体影像图和等高线图。最后,与美国、俄罗斯（前苏联）、欧空局同类型的返回式卫星进行了比较。     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

Characterization of lunar soils through spectral features extraction in the NIR

Recently launched hyper-spectral instrumentation with ever-increasing data return capabilities deliver the remote-sensing data to characterize planetary soils with increased precision, thus generating the need to classify the returned data in an efficient way for further specialized analysis and detection of features of interest. This paper investigates how lunar near-infrared spectra generated by the SIR-2 on Chandrayaan-1 can be classified into distinctive groups of similar spectra with automated feature extraction algorithms. As common spectral parameters for the SIR-2 spectra, two absorption features near 1300 nm and 2000 and their characteristics provide 10 variables which are used in two different unsupervised clustering methods, the mean-shift clustering algorithm and the recently developed graph cut-based clustering algorithm by Müller et al. (2012). The spectra used in this paper were taken on the lunar near side centering around the Imbrium region of the Moon. More than 100,000 spectra were analyzed.     

基于核部分非负矩阵分解的亚像元级地物光谱分析

为了进一步提高亚像元级地物的光谱分析精度,提出了一种基于核部分非负矩阵分解(Kernel Protection Non-negative Matrix Factorization,KPNMF)的非线性解混算法.首先通过基于凸面几何理论的端元提取方法提取纯像元端元候选像素集合,然后根据候选像素的空间纯度指数判断纯像元端元.在纯像元端元信息已知的条件下,利用核方法对部分非负矩阵分解(Protection Non-negative Matrix Factorization,PNMF)进行推广,构造相应的目标函数,推导迭代求解过程,分解求得亚像元端元光谱和所有端元的丰度.试验结果表明,提出的解混算法具有良好的非线性分解能力,解混结果优于线性解混算法.     

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。     

遥感图像飞机目标高效搜检深度学习优化算法

为了实现大幅面遥感图像中飞机目标的高效检测与准确定位,通过深度神经网络（DNN）的级联组合,提出了一种新颖的搜寻与检测相集成的飞机目标高效检测算法。首先,运用高性能的端到端DNN网络,进行停机坪与跑道区域的像素级高效精准分割,从而大幅度缩小飞机目标的搜索范围,以降低虚警发生概率,完成飞机目标候选检测区域的快速搜寻。然后,针对分割所得停机坪与跑道区域,借助手工数据集对YOLO网络模型进行迁移式强化训练,一方面可以弥补训练集在样本类型与数据规模上的不足,另一方面借助YOLO网络的强时效性优势对飞机目标的位置进行回归求解,可以显著提高飞机目标的检测效率。停机坪与跑道区域分割DNN网络在分割精度与时效性上具有显著优势,而迁移式强化训练YOLO网络不仅具有很高的检测效率,在检测精度上也能保持良好的性能。通过一系列综合实验与对比分析,验证了提出的搜寻与检测相集成的DNN级联组合式飞机目标高效检测算法的性能优势。      

不完全角度CT图像重建的模型与算法

为了提升不完全角度计算机断层成像(CT)图像的重建精度和重建效率,研究了有限角度和稀疏角度下的CT图像重建问题,提出新的全变差最小化目标函数,通过将上一步迭代重建的图像作为反馈加入到新的迭代之中,不断更新目标函数的已知项。在算法求解时,采用增广Lagrangian罚函数方法,将约束问题非约束化,并将之转化为等价的3个子问题,通过在交替方向上求解子问题来获得优化问题的最优解。实验结果表明,该算法重建出的图像信息完整,细节清晰,重建精度高,与Split Bregman算法相比,本文算法结果的相对均方误差可下降42.1%~98.5%,条纹指标可下降42.8%~98.5%。