遥感数据融合的进展与前瞻

数据融合是提升遥感影像应用能力的重要手段,一直是遥感信息处理与应用领域的研究热点。本文系统综述了遥感数据融合的进展与前瞻:首先对数据融合的层次与分类进行了总结和归纳,将遥感数据融合划分为同质遥感数据融合、异质遥感数据融合、遥感—站点数据融合、遥感—非观测数据融合4大类;在此基础上,重点针对时—空—谱光学遥感数据的融合,从多视超分辨率融合、多尺度融合、空—谱融合、时—空融合、时—空—谱一体化融合等方面进行了详细阐述;最后总结了遥感数据融合的前瞻研究方向,包括时—空—谱一体化融合的拓展、空天地观测数据的跨尺度融合、传感网环境下的在线融合、面向应用的融合方法等。     

我国高校遥感科学与技术专业现状分析

为了进一步研究新型交叉性学科——遥感科学与技术专业的建设与发展问题,该文对我国高校遥感科学与技术专业的现状进行了剖析。为适应遥感科学与技术专业人才的需求,我国已有28所高校开设了遥感科学与技术专业。此专业主要设置在理工类普通高等院校,并以测绘科学背景院校为主。不同的学科背景,专业培养目标各具特色,课程体系与结构设置差异较大。为深化遥感科学与技术专业的教学改革,科学合理地设置专业、建设课程体系,在专业的地区分布、设置院校、专业培养目标、课程体系与结构等方面尚需进一步优化。     

结合像元分解和STARFM模型的遥感数据融合

高空间、时间分辨率遥感数据在监测地表快速变化方面具有重要的作用。然而,对于特定传感器获取的遥感影像在空间分辨率和时间分辨率上存在不可调和的矛盾,遥感数据时空融合技术是解决这一矛盾的有效方法。本文利用像元分解降尺方法(Downscaling mixed pixel)和STARFM模型(Spatial and Temporal Adaptive Reflectance Fusion Model)相结合的CDSTARFM算法(Combination of Downscaling Mixed Pixel Algorithm and Spatial and Temporal Adaptive Reflectance Fusion Model)进行遥感数据融合。首先,利用像元分解降尺度方法对参与融合的MODIS数据进行分解降尺度处理;其次,利用分解降尺度的MODIS数据替代STARFM模型中直接重采样的MODIS数据进行数据融合;最后以Landsat 8和MODIS遥感影像数据对该方法进行了实验。结果表明:(1)CDSTARFM算法比STARFM和像元分解降尺度算法具有更高的融合精度;(2)CDSTARFM能够在较小的窗口下获得更高的融合精度,在相同的窗口下其融合精度也高于STARFM;(3)CDSTARFM融合的影像更接近真实影像,消除了像元分解降尺度影像中的"图斑"和STARFM模型融合影像中的"MODIS像元边界"。     

云蒙湖流域不同坡度的土壤侵蚀特征

针对定量分析土壤侵蚀在各坡度等级上的空间分布研究较少的现状,该文选用通用的土壤流失预报方程,对云蒙湖流域1986—2010年间的土壤水力侵蚀状况进行了定量的估算,以探讨不同坡度上的土壤侵蚀特征,并进一步分析了土壤侵蚀变化与人类活动的关系。分析得出:土壤侵蚀强度发生在人类活动比较频繁的区域上(8~25°坡度)更为严重;2010年比1986年强度以上所占比例在15°坡度等级上相对更低,在15°坡度等级上有所增加;云蒙湖流域主要土壤侵蚀量发生在25°坡度上;2010年比1986年耕地面积减少、林地和居民用地面积增加是土壤侵蚀降低的主要因素。     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

多波束声呐和侧扫声呐数据融合方法研究综述

多波束声呐系统与侧扫声呐系统均为海底面探测的重要工具,二者均采用声学方法,在工作原理上存在异同。本文简要介绍了二者的研究进展,分别对其数据处理进行了比对分析,认为多波束声呐处理方法侧重于数据的测量精度,而侧扫声呐则主要侧重于图像处理;归纳了当前二者主要的数据匹配融合方法,包括同名特征融合、基于SURF算法的匹配融合以及特征点融合,从数据采集原理上对数据融合方法进行了深入分析,发现在探头定位、单ping数据点分布以及ping之间的数据定位上存在一定的困难,即使经过一定的处理,二者采集的也非简单的平面图像,故二者的数据融合尚存在一定的难度。     

熔融制样-X射线荧光光谱法测定电气石中12种主次量元素

电气石是一类含硼的铝硅酸盐矿物,化学成分复杂、化学稳定性强,不易湿法分解,B_2O_3含量较高,导致其主次量元素的同时测定存在一定困难。本文采用熔融法制样,建立了X射线荧光光谱法测定电气石Na_2O、MgO、Al_2O_3、SiO_2、P_2O_5、K_2O、CaO、TiO_2、V_2O_5、Cr_2O_3、MnO、TFe_2O_3等主次量元素的分析方法。样品与四硼酸锂-偏硼酸锂-氟化锂(质量比为4.5∶1∶0.4)混合熔剂的稀释比例为1∶10,消除了粒度效应和矿物效应;在缺少电气石标准物质的情况下,选择土壤、水系沉积物及多种类型的地质标准物质绘制校准曲线,利用含量与电气石类似的标准物质验证准确度,测定结果的相对标准偏差小于4.2%。采用所建方法测定四种不同类型电气石实际样品,测定值与经典化学法基本吻合。本方法解决了电气石不易湿法分解和硼的干扰问题,测定结果准确可靠,与其他方法相比操作简便,分析周期短。     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.