单频机载激光测深海陆回波自动分类方法EI北大核心CSCD

海陆回波分类是机载激光测深中的一项波形预处理步骤,关系着后续信号检测和点云生成的精度。针对现有海陆回波分类方法不适用于单频机载激光测深系统且自动化程度不高的问题,本文提出一种单频机载激光测深海陆回波自动分类方法:首先,通过首末回波信号检测及点位计算获得回波的点云高程特征;然后,采用高程直方图拟合的方式确定平均水面位置,依据点云高程特征判定大部分回波的海陆属性,对余下的未定回波,仅保留其中的最强信号并统一处理为单信号回波,同时提取波形的信号特征和能量分布特征,依据点云高程特征的相似性自动建立训练样本集;最后,利用支持向量机分类器实现未定回波的分类。采用国产系统Mapper5000采集的实测数据进行试验,结果表明基于首末回波点云的初分类可快速、准确地对远离海陆交界处的回波进行分类,基于波形特征的未定回波分类可在自动建立的训练样本集支持下实现海陆交界处未定回波的高精度分类。与传统方法相比,本文方法无须近红外通道波形和人工样本的辅助就可以达到较高的分类精度,其中总体分类精度可达99.82%,海陆交界处分类精度可达91.59%。     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.     

以交叉累积剩余熵为准则的星载激光测高仪大光斑波形数据与地形匹配法

提出了一种基于交叉累积剩余熵的星载激光测高仪大光斑波形数据与地形匹配方法。根据星载激光测高仪大光斑回波波形信号包含地形结构信息的特性,将激光回波波形数据和数字表面模型(DSM)投影到统计特征空间,建立数据的统计特征向量,消除数据间维度差异,以交叉累积剩余熵为相似性测度匹配波形数据与地形的统计特征。试验结果表明,本方法能够较好地实现激光回波波形数据与地形的匹配,匹配精度达到一个像素以内。     

Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data

Information on Earth's land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors. In this study, we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery. For this purpose, the spectral angle mapper (SAM), the object-based and the non-linear spectral unmixing based on artificial neural networks (ANNs) techniques were applied. A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification, namely of the pixel purity index (PPI) and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites. Object-based classification outperformed the other techniques with an overall accuracy of 83%. Sub-pixel classification based on the ANN showed an overall accuracy of 52%, very close to that of SAM (48%). SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%. Yet, all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery, which affected the spectral separation among the land use/cover classes.     

Lithological mapping from hyperspectral data by improved use of spectral angle mapper

The spectral angle mapper (SAM), as a spectral matching method, has been widely used in lithological type identification and mapping using hyperspectral data. The SAM quantifies the spectral similarity between an image pixel spectrum and a reference spectrum with known components. In most existing studies a mean reflectance spectrum has been used as the reference spectrum for a specific lithological class. However, this conventional use of SAM does not take into account the spectral variability, which is an inherent property of many rocks and is further magnified in remote sensing data acquisition process. In this study, two methods of determining reference spectra used in SAM are proposed for the improved lithological mapping. In first method the mean of spectral derivatives was combined with the mean of original spectra, i.e., the mean spectrum and the mean spectral derivative were jointly used in SAM classification, to improve the class separability. The second method is the use of multiple reference spectra in SAM to accommodate the spectral variability. The proposed methods were evaluated in lithological mapping using EO-1 Hyperion hyperspectral data of two arid areas. The spectral variability and separability of the rock types under investigation were also examined and compared using spectral data alone and using both spectral data and first derivatives. The experimental results indicated that spectral variability significantly affected the identification of lithological classes with the conventional SAM method using a mean reference spectrum. The proposed methods achieved significant improvement in the accuracy of lithological mapping, outperforming the conventional use of SAM with a mean spectrum as the reference spectrum, and the matching filtering, a widely used spectral mapping method.     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.     

结合空间信息的EDCC测度光谱匹配分类方法

光谱匹配分类方法以光谱相似性测度为分类准则,一种相似性测度只对应于光谱曲线的一种特征,用于光谱匹配分类效果并不好;组合不同类型的相似性测度能够有效改善分类效果,但光谱匹配分类往往忽略了相邻像元间的相关性。为了更好地利用空间信息,提高光谱匹配分类精度,首先组合欧氏距离测度和相关系数测度,得到欧氏距离-相关系数测度;其次通过加入空间乘子,得到结合空间信息的欧氏距离-相关系数测度,从而在光谱匹配分类中增加了空间信息约束。采用两组高光谱影像进行实验验证,结果表明,相比于单一相似性测度及组合相似性测度,结合空间信息的欧氏距离-相关系数测度用于光谱匹配分类能够有效改善分类精度。     

改进的光谱角制图沿照度方向分类法及其应用——以ETM数据为例

选取多光谱遥感数据（ETM）对新疆北山西段中坡山笔架山一带进行了遥感蚀变信息提取方法研究。根据光谱角制图信息增强方法的优缺点,通过对光谱照度的研究,提出了结合光谱角分类方法进行沿光谱照度方向距离分类的信息提取方法。实验表明,该方法应用于ETM数据,增强了目标信息,同时剔除掉因照度所产生的大量非目标信息,为优化信息提供了一种有效的方法。在地形比较平坦的研究区取得了很好的应用效果。     

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.     

多种融合方法支持下的Landsat7-ETM+影像自动解译精度评价

对ETM+影像进行多种算法融合实验,除应用ERDAS软件现有的融合方法(PCA、MLT)外,还利用IDL语言编程实现了SFIM、HPF、MB等融合算法,通过多次修改SFIM、HPF、MB融合算法中滤波器窗口的大小、滤波算子的实验,达到既不产生噪声又增强了图像纹理信息的融合效果。对融合后的影像进行了相同地物样本、不同分类方法的监督分类。以2002年内蒙古土地利用遥感调查数据为评价标准(内蒙古自治区遥感与地理信息系统重点实验室提供),用总体精度(overall accuracy)、kappa系数两种评价指数综合反映各种融合算法与各种分类方法结合的分类精度,并对各种分类方法及融合算法予以评价。     

长江口EnviSat测高数据的波形分类重构分析

采用波形分类重构算法处理EnviSat卫星从2002年10月至2010年5月在长江口近岸海域28°N~32°N、121°E~125°E范围内的波形数据。该区域内海洋波形、波形后缘前端出现峰值的波形、波形后缘后端出现峰值的波形、似镜面波形和复杂波形分别占89.03%、2.95%、0.45%、3.31%和4.26%。根据不同的波形类别采用不同波形算法进行波形重构。同时,分析了不同重构算法之间的系统偏差,并据此确定OCOG算法、Threshold算法和子波形算法的最优阈值水平分别为65%、45%和50%。重构结果表明,波形分类重构算法优于其他波形重构算法,能有效改善原始海面高的精度,改善程度在16.62%~53.86%之间。此外,重构后交叉点差值小于重构前的交叉点差值,与轨迹P089、P411形成的交叉点的海面高差值由1m降低到25cm左右,其余交叉点的差值均在2~6cm。     

A combined image matching method for Chinese optical satellite imagery

Image matching is one of the key technologies for digital Earth. This paper presents a combined image matching method for Chinese satellite images. This method includes the following four steps: (1) a modified Wallis-type filter is proposed to determine parameters adaptively while avoiding over-enhancement; (2) a mismatch detection procedure based on a global-local strategy is introduced to remove outliers generated by the Scale-invariant feature transform algorithm, and geometric orientation with bundle block adjustment is employed to compensate for the systematic errors of the position and attitude observations; (3) we design a novel similarity measure (distance, angle and the Normalized Cross-Correlation similarities, DANCC) which considers geometric similarity and textural similarity; and (4) we introduce a hierarchical matching strategy to refine the matching result level by level. Four typical image pairs acquired from Mapping Satellite-1, ZY-1 02C, ZY-3 and GeoEye-1, respectively, are used for experimental analysis. A comparison with the two current main matching algorithms for satellite imagery confirms that the proposed method is capable of producing reliable and accurate matching results on different terrains from not only Chinese satellite images, but also foreign satellite images.     

The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery

In geological imaging spectrometry (i.e., hyperspectral remote sensing), surface compositional information (e.g., mineralogy and subsequently chemistry) is obtained by statistical comparison (by means of spectral matching algorithms) of known field- or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been given to comparison of the performance of the various spectral matching algorithms. Four spectral measures are presented: three that calculate the angle (spectral angle measure, SAM), the vector distance (Euclidean distance measure, ED) or the vector cross-correlation (spectral correlation measure, SCM), between a known reference and unknown target spectrum and a fourth measure that measures the discrepancy of probability distributions between two pixel vectors (the spectral information divergence, SID). The performance of these spectral similarity measures is compared using synthetic hyperspectral and real (i.e., Airborne Visible Infrared Imaging Spectrometer, AVIRIS) hyperspectral data of a (artificial or real) hydrothermal alteration system characterised by the minerals alunite, kaolinite, montmorillonite and quartz. Two statistics are used to assess the performance of the spectral similarity measures: the probability of spectral discrimination (PSD) and the power of spectral discrimination (PWSD). The first relates to the ability of the selected set of spectral endmembers to map a target spectrum, whereas the second expresses the capability of a spectral measure to separate two classes relative to a reference class. Analysis of the synthetic data set (i.e., simulated alteration zones with crisp boundaries at 1–2 nm spectral resolution) shows that (1) the SID outperforms the classical empirical spectral matching techniques (SAM, SCM and ED), (2) that SCM (SID, SAM and ED do not) exploits the overall shape of the reflectance curve and hence its outcomes are (positively and negatively) affected by the spectral range selected, (3) SAM and ED give nearly similar results and (4) for the same reason as in (2), the SCM is also more sensitive (again in positive and negative sense) to the spectral noise added. Results from the study of AVIRIS data show that SAM yields more spectral confusion (i.e., class overlap) than SID and SCM. In turn, SID is more effective in mapping the four target minerals than SCM as it clearly outperforms SCM when the target mineral coincides with the mineral phase on the ground.     

二滩、官地库区土地覆盖类型图制作方法研究

利用航空彩红外正射影像镶嵌图,用光谱角分类与目视解译相结合的方法,对二滩、官地库区进行土地利用调查工作,取得了较好的效果。     

Waveform-based point cloud classification in land-cover identification

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.     

一种改进的无人机影像拼接粗差剔除算法

利用无人机技术可以获取高分辨率影像。为了获取高精度的变换矩阵,提高影像匹配效率,本文对RANSAC算法进行了改进,加入影像的灰度信息进行约束,进一步剔除匹配粗差,最后采用均方根误差进行质量评判。为了验证算法的可靠性,选取一组山区影像和一组具有旋转偏角的建筑物影像进行验证。验证结果表明,匹配点粗差剔除率分别提高了15.15%和23.22%,本文算法的均方根误差较小,精度有显著的提高。     

基于SIFT的宽基线立体影像密集匹配

提出基于对极几何和单应映射双重约束及SIFT特征的宽基线立体影像多阶段准密集匹配算法。算法包括三个阶段：①基于特征点的空间分布和信息熵选取一定数量的最优SIFT特征点集并进行最小二乘初始稀疏匹配及立体像对的基本矩阵和单应矩阵估计;②对于其余特征,利用同名核线倾斜角及SIFT特征的尺度信息对匹配窗口的仿射变换参数进行迭代优化及变形改正、提取仿射不变SIFT特征描述符,并基于双重约束信息及欧氏距离测度进行匹配;③考虑宽基线立体影像较低的特征提取重复率,对第②步左右影像中未能成功匹配的特征点,基于双向搜索策略,采用基于盒滤波加速计算的SSD测度在变形改正后的双重约束区域中进行匹配,并对匹配结果进行加权最小二乘拟合定位。实际的宽基线立体影像试验结果证明了算法的有效性,可为后续的三维重建提供较为可靠的密集或准密集匹配点。     

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.     

结合邻域SAC-NED测度的高光谱图像分类方法

提出了一种光谱相似性测度用于高光谱图像分类方法。通过将光谱向量进行归一化处理,将计算得到的欧氏距离与光谱角余弦的值域归化到相同区间,得到光谱角余弦与欧氏距离联合测度值(SAC-NED)。在对图像像元进行分类时,以距离加权的方式将邻域像元参与中心像元SAC-NED值的计算,将像元分到SAC-NED值最大的类别。通过与其他5种常用相似性测度方法的实验结果对比表明:该算法能够提升高光谱图像分类的准确性和稳定性。     

角度分类器与距离分类器比较研究——以盐渍土分类为例

选择理想的分类器是进行遥感图像自动分类的关键。距离分类器是以已知地物类别的亮度值作为分类基准，通过比较未知类别像元与已知类别像元亮度值间的距离进行分类。角度分类器是以光谱谱线角为分类基准，通过比较n维波段空间中未知类别像元与已知类别像元光谱角度进行分类。本通过上述两种分类器对同一遥感图像进行分类，对两种分类器的分类效果进行了比较。