文中分析了水体及其它主要地物的光谱特性在SPOT影像中的表现特征 ,发现大多数地形阴影与水体的光谱特征具有一定的相似性 ,因而用单一的方法很难有效地提取山区中的水体。经过研究 ,发现用决策树分类方法 ,在各节点设计不同的分类器 ,可以有效地提取山区中的水体。由于不同类型的水体其面积、周长、形状等几何特征各有特点 ,并且与地貌类型有一定的关系 ,因而可利用水体的空间特征信息 ,对提取的水体进行分类 ,其方法也选用决策树分类方法 ,分类结果令人满意

本文在对各种遥感信息提取方法进行深入分析的基础上，提出了一种水体的遥感信息自动提取方法。该方法从遥感信息机理的角度出发，构造地物信息提取模型，并利用面向对象的设计方法建立了该模型，从而实现广义上的基于知识的专题信息提取。以ＮＯＡＡ／ＡＶＨＲＲ图像为例进行水体的自动提取，通过对自然水体和新积水区的应用研究，取得了较好的效果。可进一步应用于洪水灾害的监测。

在对Mcfeeters提出的归一化差异水体指数(NDWI)分析的基础上,对构成该指数的波长组合进行了修改,提出了改进的归一化差异水体指数MNDWI(Modified NDWI), 并分别将该指数在含不同水体类型的遥感影像进行了实验,大部分获得了比NDWI好的效果,特别是提取城镇范围内的水体.NDWI指数影像因往往混有城镇建筑用地信息而使得提取的水体范围和面积有所扩大.实验还发现MNDWI比NDWI更能够揭示水体微细特征,如悬浮沉积物的分布、水质的变化.另外, MNDWI可以很容易地区分阴影和水体, 解决了水体提取中难于消除阴影的难题.

Satellite-based remote sensing imaging can provide continuous snapshots of the Earth090005s surface over long periods. River extraction from remote sensing images is useful for the comprehensive study of dynamic changes of rivers over large areas. This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. The use of a training map from erosion morphology helps to extract the non-predictive river090005s curves in the image. The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river090005s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. The proposed algorithm was tested on the Kuala Terengganu district, Malaysia, an area that includes a river, a bridge, dam and a fair amount of vegetation. The results were compared with two standard methods based on visual perception and on peak signal-to-noise ratio, respectively. The novelty of this approach is the definition of the contextual information filtering technique, which provides an accurate extraction of river segmentation from satellite images.

The traditional active contour models can not segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency.

The Main Ethiopian Rift (MER) is an area of extreme topography underlain by post-Miocene volcanic rocks, Jurassic limestone and a Precambrian basement. A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land. We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data and geomorphologic parameters to discern patterns and features of gully erosion in the MER. Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) classifiers are used to extract different gully shapes and patterns. Several spatial textures based on Grey Level Co-occurrence Matrices (GLCMs) are then generated. Afterwards, the same classifiers are applied to the ASTER data combined with the spatial texture information. We used geomorphologic parameters extracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies鈥 shapes. The classifications show accuracies varying between 67% and 89%. Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening. This study reveals the utility of combining ASTER data and spatial textural information in discerning areas affected by gully erosion.

ABSTRACT Gully delineation is a critical aspect of accurately determining soil losses but associated methodologies are rarely detailed. Here, we describe a new gully mapping method, the normalized topographic method (NorToM), based on processing digital elevation model (DEM) data, and we assess associated errors when it is applied over a range of geomorphological scales. The NorToM is underpinned by two gully detection variables (normalized slope and elevation) calculated over local windows of prescribed size, and a group of filtering variables. For four study sites, DEMs of gullies were obtained using field and airborne photo-reconstruction and evaluated using total station and differential global positioning system (dGPS) survey. NorToM provided accurate areal and volume estimates at the individual gully scale but differences increased at the larger gully system and gully network scales. We were able to identify optimal parameters for using the NorToM approach and so confirm that is represents a useful scale-independent means of gully mapping that is likely to be valid in other environments. Its main limitations are that the normalization process might be time-consuming at regional scales and the need for a fixed window size when applied to landforms with extreme variations in dimensions. Copyright 2014 John Wiley & Sons, Ltd.

This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV) remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1) the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2) at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM) and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective.

Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed iaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed iaoli anomaly detector and Uniform Target Detector (RXD TD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

提出了一种用于高光谱图像目标探测的预处理方法, 目的是提高目标光谱的准确性, 进而提高有监督目标探测算法的精度.该方法将实验室或野外获取的目标光谱作为参考信号, 利用带参考信号的独立分量分析方法, 从图像中提取出与参考信号相关性最大的独立分量作为新的目标光谱.仿真和实际高光谱数据的实验结果表明, 所提出的方法能较大提高目标光谱的准确性, 从而较大提高目标探测算法的精度.

提出一种基于独立分量分析(ICA)的高光谱图像目标检测算法。首先利用无监督正交子空间投影进行端元提取,并将端元矢量构成矩阵作为快速定点独立分量分析的初始化混合矩阵,解决了独立分量在排序上的随机性;同时采用基于噪声调整的主分量分析(NAPCA)对原始图像数据降维,继而采用初始化后的快速独立分量分析从保留的主分量中依次提取出目标。利用AVIRIS高光谱数据进行实验研究,结果表明提出的算法能够有效地提取图像中的目标信息,其性能优于改进的CEM检测算法。

数学形态学是一种非线性滤波方法形态和差运算,即膨胀与腐蚀是数学形态学的基础.数学形态学已由二值形态学、灰度形态学、软数学形态学、模糊形态学发展到模糊软形态学,可用于抑制噪声、特征提取、边缘检测、图象分割、形状识别、纹理分析、图象恢复与重建等图象处理问题,在图象处理领域得到了越来越广泛的应用.本文结合目前的研究进展,对数学形态学的理论研究及其应用进展进行综合性阐述.

Coalbed Natural Gas extraction usually results in the production of excess, or product, water, necessitating a strategy for disposal and minimizing landscape and habitat impacts. In the Powder River Basin in Wyoming, product water is either discharged into ephemeral streams or retention/detention ponds. Monitoring these water bodies is important for environmental, habitat, and human health perspectives. This study assessed the benefits of using higher spatial resolution ASTER image, in contrast to more commonly used moderate-resolution Landsat imagery, for detecting smaller water bodies in the Powder River Basin. ASTER and Landsat Thematic Mapper (TM) images were acquired concomitantly and classified following similar methods to identify water bodies for three color classes and a range of sizes. Results showed that the ASTER image had significantly higher accuracies for detecting clear and green colored water bodies, but did not demonstrate significant improvement for detecting turbid water bodies. ASTER also showed significant improvements in detecting small-scale water bodies. However this improved performance was somewhat offset due to the misclassification of other landscape elements as water in the ASTER image. Overall when compared to Landsat TM image, ASTER image more accurately detected more water bodies, especially those with a relatively small surface area, with the two images producing similar results at large scales. The application of ASTER is therefore appropriate for monitoring and evaluation of water bodies in the Powder River Basin and elsewhere.

介绍了以ASTER遥感数据为信息源,通过遥感图像处理技术自动提取天山南麓木扎尔特流域的DEM.根据已建立的数字高程模型,在A rc/Info模块支持下,自动提取流域水系的参数,如栅格水系流向、自动生成河网等,并探讨了寒区流域水系的参数化过程.结果表明,部分冰川覆盖的槽谷地形(U形)区被误判为流域水系,其误差率为21.1%;经过剔除冰川覆盖区误判以后的水系再与来自1¨100 000地形图的水系长度相比,误差仅为0.9%;另外,由于两者数据源的获取时间不同,河流位置也略有差异,这可能与河道的摆动或改道相关,但来自ASTER影像提取水系更能反映河道的近期信息.总体上讲,ASTER数据能适合寒区流域水系参数化的需要,为水文定量化研究及应用提供数据基础.

A mathematical morphology based drainage line extraction method from remote sensing image is proposed.The coarse drainage line is first extracted with spectral pattern recognition approach,and then is subject to processes such as drainage line connection,noise removal and drainage line thinning using mathematical morphology transformations.A subset of landsat ETM+ image is used to test the presented method.The result shows that this method can effectively extract continuous and smooth drainage lines.Further comparison is made between the extracted drainage lines and that of the digital line graph(DLG) with reference of remote sensing image.It demonstrated that the drainage lines by the proposed method are better agreed with the image than that of DLG.