An improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery

In recent years, application of remote sensing to marine mammal surveys has been a promising area of investigation for wildlife managers and researchers. In April 2006, the United States and Russia conducted an aerial survey of Pacific walrus (Odobenus rosmarus divergens) using thermal infrared sensors to detect groups of animals resting on pack ice in the Bering Sea. The goal of this survey was to estimate the size of the Pacific walrus population. An initial analysis of the U.S. data using previously-established methods resulted in lower detectability of walrus groups in the imagery and higher variability in calibration models than was expected based on pilot studies. This paper describes an improved procedure for detection and enumeration of walrus groups in airborne thermal imagery.Thermal images were first subdivided into smaller 200 × 200 pixel “tiles.” We calculated three statistics to represent characteristics of walrus signatures from the temperature histogram for each tile. Tiles that exhibited one or more of these characteristics were examined further to determine if walrus signatures were present. We used cluster analysis on tiles that contained walrus signatures to determine which pixels belonged to each group. We then calculated a thermal index value for each walrus group in the imagery and used generalized linear models to estimate detection functions (the probability of a group having a positive index value) and calibration functions (the size of a group as a function of its index value) based on counts from matched digital aerial photographs.The new method described here improved our ability to detect walrus groups at both 2 m and 4 m spatial resolution. In addition, the resulting calibration models have lower variance than the original method. We anticipate that the use of this new procedure will greatly improve the quality of the population estimate derived from these data. This procedure may also have broader applicability to thermal infrared surveys of other wildlife species.     

热带森林植被的动态变化遥感监测

本文讨论了以热带森林植被为主体的再生资源的面积动态变化监测。研究中包括两个部分。首先,我们利用多时相遥感图像对大面积的西双版纳州进行地类判读,系统地分析了森林植被的动态变化。其次,利用Landsat MSS和TM数据对自然保护区的动态变化进行了包含无监督分类和归一化差值植被指数分析的数字图像处理,变化分类也相当符合实际。总的实验结果表明,这种监测方法是很有效的,可在再生资源监测中特别是在森林植被监测中加以推广应用。     

Analyzing fine-scale wetland composition using high resolution imagery and texture features

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).     

On-the-fly server-based aerial imagery management system

Aerial images are valuable products when dealing with both geospatial and geotemporal analysis. Nowadays, they are widely used for many different purposes and by an extensive public, including private companies, official administrations and individual users. Although in the past few years there has been an increasing interest in showing all kinds of geographical information on the World Wide Web, access to aerial imagery and its dissemination are still difficult and lack flexibility. This paper introduces an aerial imagery management system based on client–server principles, operated so as to allow users quick and efficient queries, processing and management of huge sets of photogrammetric imagery stored on raster servers. It is a novel product that is ready to provide image-based cartographic data available in public and private digital warehouses, facilitating all the required visualisations and queries, as well as geometric and radiometric processing on the fly. This paper shows the design, system architecture and various functionalities of the system in a real-life scenario.     

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications.     

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

Image Sources for Informal Settlement Management

Informal settlements are a common feature of developing countries. In South Africa the improvement of living conditions in these settlements and the upgrading to formal housing types are regarded as being of central importance to the nation's development. Effective settlement improvement and upgrading activities, which we here term "management", require adequate spatial data. To date, the acquisition of spatial models of informal settlements has been based on conventional mapping techniques, and mostly on photogrammetry. Data are compiled using analogue or analytical methods. These are manual and hence require both considerable expertise and expensive equipment. Moreover, these methods are uneconomical over the often relatively small, densely populated areas covered by informal settlements and are also too expensive to employ with a regularity required to support such tasks as change detection. Alternative imaging sources and mapping techniques are therefore needed.
In this article we examine the problem of spatial information acquisition for informal settlement management from three perspectives: spatial information requirements, the role which imagery can play in satisfying these spatial information requirements, and effective imaging options. We focus on the potential of high resolution satellite imaging, small format digital aerial imagery and digital multispectral video systems for rapid mapping. We also discuss the example of automated shack extraction from aerial imagery.     

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products,using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.     

A block Karhunen–Loève Transform based efficient lossy compression algorithm for optical remote sensing imagery

In present study, a block Karhunen–Loève Transform (KLT) based efficient lossy compression algorithm for optical remote sensing imagery is proposed. A Discrete Wavelet Transform (DWT) is performed on each band of the imagery to remove the spatial correlation. Each band of the imagery is decomposed into non-overlapping blocks of similar size and the transform coefficients of each block in the wavelet domain are treated as a single object. A rate-distortion optimization is introduced to perform rate allocation of multiple bands. Each band is partitioned into code-blocks. The embedded block coding with optimized truncation algorithm is executed on the code-blocks to produce final bit-stream. The complexity of the proposed algorithm is compared with global KLT-DWT. The result reports the complexity of JPEG 2000 (Part 1) is lowest with encoding time 114 ms as compared to global KLT-DWT (N = 1024), global DWT-KLT (N = 1024), block-based DWT-KLT (N = 512), block-based DWT-KLT (N = 256), block-based DWT-KLT (N = 128), block-based DWT-KLT (N = 64) and block-based DWT-KLT (N = 32).     

面向对象的高分辨率遥感影像地理要素提取

应用面向对象影像分类方法进行空间目标特征提取和分析,实现利用遥感影像建立与更新地理空间数据库,对于正在进行的数字城市建设和国情监测具有重要的意义和作用。本文阐述了高空间分辨率影像特征提取的关键技术,采用面向对象的特征提取技术和影像分类方法,开展了基于ADS40航空影像的地理要素提取实验,获得了比较满意的专题信息。     

Detecting Squarrose Knapweed (Centaurea virgata Lam. Ssp. squarrosa Gugl.) Using a Remotely Piloted Vehicle: A Utah Case Study

Determining the location of founding weed populations is critical to minimizing the diffusion of weedy species. Remote sensing is a promising tool for early detection of these small weed patches. The objective of this study was to determine the capability of a small remotely piloted vehicle (RPV) (carrying a digital camera and GPS) to acquire aerial photography from which small infestations of squarrose knapweed (Centaurea virgata Lam. Ssp. squarrosa Gugl.) could be detected and mapped. Two Utah rangeland sites were studied. The location of squarrose knapweed found on the digital air aerial photography (true color) was compared to a complete census of knapweed conducted on the ground. Although the two study sites had different vegetation species mixes, site histories, and soil conditions, the results were comparable. Despite the large scale of the aerial photography, the knapweed detection rate on the spring photography was only 5%. In contrast, knapweed detection rates on late summer imagery were about 50%. False alarm rates at all seasons were extremely low. Despite the capability shown for weed detection with the RPV and digital camera system, the practical difficulties of using a small RPV in the field requires more research before the system can be operationalized for weed management tasks.     

Insights into the affine model for high-resolution satellite sensor orientation

As a model for sensor orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward linear model, comprising only eight parameters, which has been shown to yield sub-pixel geopositioning accuracy when applied to Ikonos stereo imagery. This paper aims to provide further insight into the affine model in order to understand why it performs as well as it does. Initially, the model is compared to counterpart, ‘rigorous’ affine transformation formulations which account for the conversion from a central perspective to affine image. Examination of these rigorous models sheds light on issues such as the effects of terrain and size of area, as well as upon the choice of reference coordinate system and the impact of the adopted scanning mode of the sensor. The results of application of the affine sensor orientation model to four multi-image Ikonos test field configurations are then presented. These illustrate the very high geopositioning accuracy attainable with the affine model, and illustrate that the model is not affected by size of area, but can be influenced to a modest extent by mountainous terrain, the mode of scanning and the choice of object space coordinate system. Above all, the affine model is shown to be both a robust and practical sensor orientation/triangulation model with high metric potential.     

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.     

多传感器影像配准中基于虚拟匹配窗口的SIFT算法

提出了一种基于虚拟匹配窗口的SIFT算法,通过构建虚拟匹配窗口,增大SIFT特征点之间的尺度相似性,提高了匹配的几率;并通过与最小二乘法和双线性内插法的结合完成自动配准。文中选取角度和尺度偏差较大的SPOT-5(P)与TM影像进行实验,结果表明,配准精度小于一个像素。     

Employing airborne multispectral digital imagery to map Brazilian pepper infestation in South Texas

A study was conducted in south Texas to determine the feasibility of using airborne multispectral digital imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral digital imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral digital imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.     

基于像元分析的影像空间信息变化探测

介绍了影像的基本属性,区域和像元的空间信息变化探测方法,重点阐述了基于像元分析变化的探测原理和应用方法,针对不同的方法进行了实验并给出了实验的结果。     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

Role of aerial photographs and landsat imagery in forest resources surveys

Many of the data needs for efficient management of forest resources can be met by aerial photographs. Commercially important tree species can be distinguished from other less important miscellaneous species with the help of aerial photographs. Forests can be classified according to their height and density classes. Aerial photographs have become indispensable for mapping of forests and preparation of forest inventories. A comparison of interpretation results obtained from landsat imagery and aerial photographs (1 ∶ 10,000 Black and White panchromatic photography) with respect to forestry interpretation is given. It is pointed out that the imagery obtained from satellities can be used for reconnaissance of a region and for deciding the priorities for carrying out more detailed surveys of forest resources with the help of air photointerpretation techniques     

FedIDS: a federated cloud storage architecture and satellite image delivery service for building dependable geospatial platforms

Earth observation satellites produce large amounts of images/data that not only must be processed and preserved in reliable geospatial platforms but also efficiently disseminated among partners/researchers for creating derivative products through collaborative workflows. Organizations can face up this challenge in a cost-effective manner by using cloud services. However, outages and violations of integrity/confidentiality associated to this technology could arise. This article presents FedIDS, a suite of cloud-based components for building dependable geospatial platforms. The Fed component enables organizations to build shared geospatial data infrastructure through federation of independent cloud resources to withstand outages, whereas IDS avoids violations of integrity/confidentiality of images/data in sharing information and collaboration workflows. A FedIDS prototype, deployed in Spain and Mexico, was evaluated through a study case based on a satellite imagery captured by a Mexican antenna and another based on a satellite imagery of a European observation mission. The acquisition, storage and sharing of images among users of the federation, the exchange of images between Mexican and Spanish sites and outage scenarios were evaluated. The evaluation revealed the feasibility, reliability and efficiency of FedIDS, in comparison with available solutions, in terms of performance, storage consume and integrity/confidentiality when sharing images/data in collaborative scenarios.     

Comparative study on projected clustering methods for hyperspectral imagery classification

In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction.