Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

Principal component analysis (PCA) is one of the most commonly adopted feature reduction techniques in remote sensing image analysis. However, it may overlook subtle but useful information if applied directly to the analysis of hyperspectral data, especially for discriminating between different vegetation types. In order to accurately map an invasive plant species (horse tamarind, Leucaena leucocephala) in southern Taiwan using Hyperion hyperspectral imagery, this study developed a spectrally segmented PCA based on the spectral characteristics of vegetation over different wavelength regions. The developed algorithm can not only reduce the dimensionality of hyperspectral imagery but also extracts helpful information for differentiating more effectively the target plant species from other vegetation types. Experiments conducted in this study demonstrated that the developed algorithm performs better than correlation‐based segmented principal component transformation (SPCT) and conventional PCA (overall accuracy: 86%, 76%, 66%; kappa value: 0.81, 0.69, 0.57) in detecting the target plant species, as well as mapping other vegetation covers.     

Early detection of the invasive alien plant Solidago altissima in moist tall grassland using hyperspectral imagery

We assessed aerial hyperspectral imagery with high spatial (1.5 m) and spectral (8.9 nm) resolutions for detecting and mapping the early invasion by Solidago altissima of understory vegetation in moist tall grassland. Generalized linear models (GLMs) were constructed to predict S. altissima occurrence using 1.5 m pixels from hyperspectral data collected during the spring when understory vegetation was directly observable from above. A data set of presence–absence derived from percentage cover data was used for the analyses. The values of the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.77–0.87 in the validation data set. Three minimum noise fraction (MNF) bands differentiated S. altissima in the best-performing model (selected based on Akaike's information criterion) for the occurrence of S. altissima. The results suggest that the aerial hyperspectral images obtained during spring before the seasonal development of the grass canopy are useful for the early detection and mapping ofS. altissima invading moist tall grassland.     

Temporal analysis of forest structural condition at an acid mine site using multispectral digital camera imagery

A large abandoned tailings deposit at a mine site near Timmins, Ontario, Canada has produced significant damage in an adjacent forest due to contamination and wind stress. Significant forest structure changes were measured between 1997 and 1999. A multivariate image-based forest structure index (FSI) was developed using canonical correlation analysis of 1997 field and airborne digital camera data. FSI included decreasing canopy closure and leaf area index, and increasing blown down and standing dead structure measures associated with image spectral, textural and radiometric fraction variables. An image model predicting FSI achieved an R ²=0.66. The model equation was then applied to 1999 airborne imagery to predict FSI for each plot. Comparing the 1999 image predicted FSI to that calculated from field data showed that the model was strong in predicting positive or no forest structure changes, but not increased structure degradation. The latter was due to the presence of herbaceous and shrub vegetation that had developed during the two-year period in open plots near the tailings where blow down was significant. The next research phase will derive means to separate these two signals in forests of open overstory.     

Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.     

Multitemporal Landsat TM imagery analysis for mapping and quantifying degraded rangeland in the Bahurutshe communal grazing lands,South Africa

Multitemporal remote sensing was used to map and quantify rangeland degradation in communal grazing lands of Lehurutshe district, northwestern South Africa. Based on established theory that veldt degradation ultimately results in bare land in addition to loss and replacement of palatable rangeland species, rangeland bare land was used as an indicator of degradation, primarily due to lack of palatable rangeland species spectral signatures. Using a January 1989 image as the base year, in which the rangelands were healthier, January 1995 and 2005 Landsat Thematic Mapper (TM) images were used for mapping and quantifying degradation, with the hypothesized degradation status that bare land in the rangelands would not have emerging grass just after the start of the summer rains. Image processing involved geometric registration, hybrid classification and geographic information system (GIS) overlay analysis. The results indicate moderate rangeland degradation, up to 4% area, particularly in the district's more inhabited south. Although the amount of degradation is moderate, the degradation has significant localized effects in this semiarid environment. Remote sensing techniques appear vital for rapid rangeland and other multitemporal spatial analyses in the area and the southern Africa subregion in general, to be taken advantage of with the launch of South Africa's environmental satellite.     

Machine annotation and retrieval for digital imagery of historical materials

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University. James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University. Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans.     

The use of high resolution digital camera imagery to characterize the distribution of Pteronia incana invader species in Ngqushwa (formerly Peddie) District,Eastern Cape,South Africa

High resolution infrared digital camera imagery was used to determine the extent of the invasion of hill slopes by Pteronia incana, an unpalatable dwarf shrub. On the basis of a priori field surveys, the imagery was classified into different degrees of the invasion and other surface cover types using Idrisi32 GIS. The imagery was noted to have limited constraints, as evidenced by the high level of classification accuracy. A range of vegetation indices was examined to identify one that best characterized the spatial distribution of the shrub and its degree of invasion. Distinct spectral separability of the invader from other surface cover types was achieved by means of the perpendicular vegetation index (PVI), as opposed to the ratio based vegetation indices (NDVI, SAVI, and MSAVI). Depending on the local geographical characteristics, the PVI could be particularly suited for the identification of perennial shrubs with characteristics similar to P. incana, which are usually interspersed with considerable bare zones.     

An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas

Glacial lakes in alpine regions are sensitive to climate change. Mapping and monitoring these lakes would improve our understanding of regional climate change and glacier-related hazards. However, glacial lake mapping over large areas using remote sensing remains a challenge because of various disturbing factors in glacial and periglacial environments. This article presents an automated mapping algorithm based on hierarchical image segmentation and terrain analysis to delineate glacial lake extents. In this algorithm, each glacial lake is delineated with a local segmentation value, and the topographic features derived from digital elevation models (DEMs) are also used to separate mountain shadows from glacial lakes. About 100 scenes of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images from circa 1990, circa 2000 and 2009 were used to map the glacial lakes and their changes over the entire Himalayas. The results show that the algorithm can map the glacial lakes effectively and efficiently. Mountain shadows or melting glaciers can be differentiated from glacial lakes automatically, and those lakes with mountain shadows can also be identified. Area changes of more than 1000 glacial lakes show that the glacial lakes in the Himalayas have experienced mixed directions of change, while the overall lake areas are expanding at an accelerated rate in the past two decades, indicating great changes to the glacial lakes in the Himalayas.     

The use of SPOT-simulated imagery in hydrological mapping

Abstract

The fine spatial resolution offered by the SPOT system promises great improvements in the mapping of surface water and drainage networks. Simulated SPOT imagery of sites covering a variety of landscape units in Wales were examined. Identification of streams on the basis of spatial characteristics alone was found to be inefficient, and the use of thematic information was found beneficial. Strategies for interactive image enhancement are discussed, and the results of a visual interpretation of standard photoproducts are presented. The results of the interpretation of simulated SPOT data are compared with results of an interpretation of LANDSAT MSS data.     

Evaluation of cross-track illumination in EO-1 Hyperion imagery for lithological mapping

Hyperspectral remote sensing data is a powerful tool for discriminating lithological units and for the preparation of mineral maps for alteration studies. The spaceborne hyperspectral Hyperion sensor, despite its narrow swath width (～7.5 km), possesses great potential with its 196 channels within the wavelength range 426.82–2395.50 nm. Although it has many advantages such as low cost and on-demand coverage, much uncertainty exists in the utility of its applications. For example, poor signal-to-noise ratio, the presence of sensor-specific defects and thicker atmospheric column due to its spaceborne platform makes certain environmental and geological applications difficult or impossible. In this article we demonstrate these calibration-related uncertainties, which are manifest from the preprocessing stage to the classification stage. In addition, the intimate mixing of minerals within specific targets, for example within individual outcropping lithological units or endmembers, adds uncertainty to our spectral discrimination results. The aim of this study was to develop and evaluate an approach for geological mapping of outcrops with Earth Observing-1 (EO-1) Hyperion data. Atmospheric corrections and correction for cross-track illumination (CTI) variations (smile) were determined at different wavelength regions: the visible–near-infrared (VNIR; 420–1000 nm) and shortwave infrared (SWIR; 1000–2400 nm) regions. Our methodology was tested in a selected site at Central Anatolia, Turkey containing minimal vegetation cover. The results obtained from the image analyses were then compared and assessed with field observations and spectral measurements.     

Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery

IKONOS 1-m panchromatic and 4-m multispectral images were used to map mangroves in a study site located at Punta Galeta on the Caribbean coast of Panama. We hypothesized that spectral separability among mangrove species would be enhanced by taking the object as the basic spatial unit as opposed to the pixel. Three different classification methods were investigated: maximum likelihood classification (MLC) at the pixel level, nearest neighbour (NN) classification at the object level, and a hybrid classification that integrates the pixel and object-based methods (MLCNN). Specifically for object segmentation, which is the key step in object-based classification, we developed a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. A comparison of BD values at the pixel level and a series of larger scales not only supported our initial hypothesis, but also helped us to determine an optimal scale at which the segmented objects have the potential to achieve the best classification accuracy. Among the three classification methods, MLCNN achieved the best average accuracy of 91.4%. The merits and restrictions of pixel-based and object-based classification methods are discussed.     

Suitability of SAR imagery for automatic flood mapping in the Lower Mekong Basin

Flood detection and inundation mapping are amongst the most important applications for remote-sensing data. Space-borne radar systems, synthetic aperture radar (SAR) in particular, and its application for waterbody mapping have recently been subject to research in many publications. Although very good results have been achieved with such data, in some cases automatic waterbody classification based on SAR data is not feasible. Factors influencing the applicability are, e.g., local environmental conditions, roughening of water surfaces due to wind, or the satellite observation geometry. In this study, a measure for the usability of SAR imagery for flood mapping was investigated. Additionally, a method for permanent waterbody mapping was introduced. The study is based on Envisat ASAR wide swath mode (150 m spatial resolution) data of the Mekong River Basin. For the usability measure, the concept of ‘high-contrast tiles’ was established, which allows an a priori estimation of the expected accuracy of a waterbody classifier. The SAR-based permanent waterbody map was used for the validation of the approach. It was found that, for the test site, the new SAR usability measure allows the identification of unsuitable scenes with a certainty of more than 90%. The method is expected to be very useful for near-real-time flood mapping applications where human interaction is neither desired nor feasible when large regions and large data volumes are considered.     

一种用于数码相机自拍系统的人体检测方法

在研发一种数码相机智能自拍系统的基础上,提出了一种简单有效的基于静止背景下目标人体的检测方法。该方法基于实时帧与参考背景帧的差分建立背景模型,进行前景目标分割,并基于颜色向量夹角消除阴影的影响,最后用投影阈值分析提取目标人体,并进行定位。该方法计算量低,环境适应性强,能满足数码相机自拍模式的目标跟踪要求。     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

Design of fast logarithmic converters with high accuracy for digital camera application

Microsystem Technologies - In this paper, fast logarithmic converters with high accuracy are proposed. In previous works, using logarithmic number system (LNS)—based units can help to reduce...     

Using decision fusion of feature selection in digital forensics for camera source model identification

Digital forensics, which identifies the characteristics and origin of a digital device, has become a new field of research. If digital content will serve as evidence in court, similar to its non-digital counterparts, digital forensics can play a crucial role in identifying the source model or device. To achieve this goal, the relationship between an image and its camera model will be explored. Various image-related and hardware-related features are utilized in the proposed model by a support vector machine approach along with decision fusion techniques. Furthermore, the optimum feature subset to achieve the highest accuracy rate is also explored.     

Design of mirror system in digital single lens reflex camera for high-speed continuous shooting

This research explores anti-vibration mechanisms for mirror systems in DSLR cameras through numerical methods such as explicit/implicit finite element analysis (FEA) and multi-body dynamics (MBD). Kinematic design of the movable mirrors, which guide incoming light, is carried out through MBD, and a two-dimensional mirror system geometry that increase the mirror area while allowing fast swing-up speeds can be obtained. The dynamic behaviors of mirror systems are predicted via explicit FEM and verified with modal analysis using implicit FEM. In addition, a prototype based on FEA results has been manufactured, and improved anti-vibration performance is confirmed via measurements of the dynamic behavior of the mirror system.     

Impact of pixel size on mapping surface water in subsolar imagery

We observed surface water in a wetland, imaging in the subsolar or specular direction the exceptionally bright specular reflection of sunlight at a ground resolution of 0.3 m. We then simulated ground resolutions between 1.7 m and 1.2 km through aggregation of the 0.3 m pixels. Contrary to the expectations of some of our colleagues in the wetlands community, for these data, the accuracy of spectral mixture analysis (SMA) estimates of surface water increases as pixel ground footprint size increases. Our results suggest that regional to global scale assessments of flooded landscapes and wetlands that do not involve issues requiring 1 m resolution per se may be addressed with acceptable accuracy by applying SMA techniques to low resolution imagery. Our results indicate within-pixel estimates of surface water area derived from data measured by subsolar viewing sensors with large ground pixel footprints, such as satellite POLarization and Directionality of Earth Radiance (POLDER) data, may be highly accurate under strong surface wind conditions.     

数码监控系统磁盘阵列的扩展及数据优化管理

近年来,数码相机监控系统以其高分辨率、方便、快捷、处理软件丰富以及高性能比等优点,愈来愈受到人们的关注。但是该系统仍然存在着一系列有待继续研究的问题,比如存储数据量大、不易管理等。针对以上不足,在介绍数码相机监控系统软、硬件的设计和开发过程同时,着重讨论数码相机监控系统磁盘阵列存储系统的扩展和图像采集数据的管理等问题。