地球质心运动的频谱分析方法

针对地球质心运动主要是由多种周期运动叠加的特点，依次讨论了谱分析方法中的AR模型谱分析和小波谱分析方法，分别指出：将参数谱分析方法中的AR模型谱分析和非参数谱分析中的小波谱分析相结合产生的小波－AR谱分析以及小波包－AR谱分析方法，在地球质心运动的谱分析中有较好的频率分辨率以及噪声抑制能力。     

煤田火区烧变岩光谱特征分析及其信息提取

中国北方煤田煤层自燃现象非常严重.利用遥感手段可以快速实现火区的动态监测,及时为灭火工程提供信息.烧变岩作为火区的地表指示性特征,是火区解译最直观的信息.本文从烧变岩的反射光谱曲线特征入手,对几种提取烧变岩信息的方法进行比较,从而确定解译烧变岩的最佳方法,为圈定火区的范围和位置奠定基础.     

Introducing sensor spectral response into the classification process

Many sensors have their bands overlapped and therefore do not set a normal space. If a spectral distance is measured, as in first-order statistical classifiers, the direct consequence is that the result will not be the most accurate. Image classification processes are independent of the spectral response function of the sensor, so this overlap is usually ignored during image processing. This paper presents a methodology that introduces the spectral response function of sensors into the classification process to increase its accuracy. This process takes place in two steps: first, incident energy values of the sensors are reconstructed; second, the energy of the bands is set in an orthonormal space using a matrix singular value decomposition. Sensors with and without overlapping spectral bands were simulated to evaluate the reconstruction of energy values. The whole process was implemented on three types of images with medium, high and very high spatial resolution obtained with the sensors ASTER, IKONOS and DMC camera, respectively. These images were classified by ISODATA and minimum distance algorithms. The ISODATA classifier showed well-defined features in the processed images, while the results were less clear in the original images. At the same time, the minimum distance classifier showed that overall accuracy of the processed images increased as the maximum tolerance distance decreased compared to the original images.     

A new Likelihood Ratio for supervised classification of fully polarimetric SAR data: An application for sea ice type mapping

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data. The derived Bayes risk function is based on the complex Wishart distribution. Furthermore, a new spatial criterion is incorporated with the LR classification process to produce more homogeneous classes. The application for Arctic sea ice mapping shows that the LR and the proposed spatial criterion are able to provide promising classification results. Comparison with classification results based on the Wishart classifier, the Wishart Likelihood Ratio Test Statistic (WLRTS) proposed by Conradsen et al. (2003) and the Expectation Maximization with Probabilistic Label Relaxation (EMPLR) algorithm are presented. High overall classification accuracy of selected study areas which reaches 97.8% using the LR is obtained. Combining the derived spatial criterion with the LR can improve the overall classification accuracy to reach 99.9%. In this study, fully polarimetric C-band RADARSAT-2 data collected over Franklin Bay, Canadian Arctic, is used.     

利用时空信息的单波段地表温度遥感反演

地表温度被认为是影响生态系统的关键因子之一,它与许多地表过程有关。目前,热红外卫星遥感技术是获取有关区域和全球尺度地表温度信息的一个有效、可行的手段。针对不同卫星上搭载的热红外传感器,许多学者开展了大量的研究,其中针对单波段热红外的特点(如Landsat TM/ETM+,CBERS和HJ-1B)提出了单通道(或单窗)算法。该类算法需要准确的地表比辐射率和大气参数(如大气水分含量)。这些参数在现实中又很难轻易获得,从而在一定程度上限制了现有算法的应用。针对HJ-1B高回访频率的特点,本文提出了利用多时相影像的时空信息来直接反演地表温度的Multi-Temporal and Spatial Information-Based Single Channel(MTSC),以解决现有算法对地表比辐射率和大气参数的过度依赖性。实例分析结果显示,基于MTSC法由HJ-1B反演得到的地表温度结果与MODIS地表(陆表和海表)温度产品具有很好的空间一致性;HJ-1B的陆表温度结果总体上被高估了约1 K,而海表温度结果总体上被高估了0.5 K;同时,MTSC法得到的HJ-1B地表温度结果具有更好的细节和空间完整性。最后,通过分析和讨论指出了一些可能的完善途径,如相似像元的确定、修改优化求解中的目标函数、参数的自适应初始化等,以便提高MTSC法的反演精度和实用性。     

Point set generalization based on the Kohonen Net

Point set generalization is one of the essential problems in map generalization. On the demands analysis of point set generalization, this paper proposes a method to generalize point sets based on the Kohonen Net model; the standard SOM algorithm has been improved so as to preserve the spatial distribution properties of the original point set. Examples illustrate that this method suits the generalization of point sets.     

Spectral material mapping using hyperspectral imagery: a review of spectral matching and library search methods

Imaging spectroscopy is an emerging and versatile technique that finds applications in diverse fields concerned with remote identification, discrimination and mapping of materials. The large amount of spectral data produced by hyperspectral imaging necessitates the development of automated techniques that convert imagery directly into thematic maps. Spectral library search method, a method of choice for organic compound identification by the mass spectroscopy, has caught the attention of researchers as one of the appropriate methods for an efficient exploitation of high quality spectral data available from the hyperspectral imaging systems. Given the apparent increase in the number of papers appearing on the subject as well as the variety of methods proposed, it is reasonable to say that the field of automated interpretation of reflectance spectral data has passed its infancy now gaining important space in the scientific community. We present an overall view of the literature relevant to the development of library search method, the various search algorithms and systems available in the purview for developing an automated hyperspectral data analysis system for material identification.     

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.     

Delineation of coalfield surface fires by thresholding Landsat TM-7 day-time image data

Surface fires are common in coalfields where coal is mined or exposed to sunlight for long durations of time. The heat energy emitted from these fires affects the signal recorded by sensors operating in the shortwave infrared regions of the electromagnetic spectrum. The Landsat TM/ETM+ band-7 is sensitive to solar reflected radiation as well as emitted radiation from a target. The ‘maximum solar reflection threshold’ method proposed in this study uses the highest spectral radiance that can be attributed to solar reflection as the conservative threshold to segregate the pixels with emitted component from those with reflected component of the EM energy. Investigations with Landsat TM/ETM+ data indicate a reflectance value of 0.23–0.25 as the most representative highest reflectance (threshold) in coal mining areas. The method apparently has the advantage that it is based on the reflectance characteristics of materials (sandstone-shale mixtures) typically found in coal mining areas.     

岷江上游流域环境脆弱性评价

以岷江上游流域TM遥感数据和地形数据为主要数据源,结合其他统计相关资料,尝试运用层次分析法(analytic hierarchy process,AHP)对复杂因素构成的脆弱性特征进行层次分解和重新构造,建立多目标要素的综合评价指标体系和模型。选取植被指数变化率、人口密度、地形起伏度、坡度及土壤类型5个要素作为评价指标因子,通过AHP确定各评价指标的权重,在Arc GIS平台下将各指标图层进行叠加分析,得出岷江上游区域环境脆弱性评价图,再根据环境脆弱性指数阈值分级,将研究区环境脆弱度分为5级。结果表明:岷江上游环境脆弱性表现较为强烈,自然因素和人文因素是造成该环境脆弱性的本质原因,该方法为岷江上游流域环境脆弱性模式和生态环境综合评价提供了一种定量分析的思路。