Search

This paper describes a method for predicting the locations and heights of the ten tallest trees in the Tennessee portion of the Great Smoky Mountains National Park. Iterative computation tools were utilized to process the data along with the lidarderived bare earth digital elevation models and digital surface models to create canopy height models for the Tennessee portion of the park. A height threshold of 51.8 meters was chosen as the minimum value for a tree of extraordinary height. Ten potential sites containing tall trees were identified using this methodology, and seven of the top ten ranking trees’ heights were field measured using accepted forestry methodology. The trees detected using these methods are potentially the tallest trees ever measured on the East Coast of the United States. These methods show that unique tall trees can be successfully detected in a large, heterogeneous forest area using lidar data.

Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

To address the problem of weak robustness against geometric attacks of remote sensing images' digital watermarking, a robust watermark- ing algorithm based on template watermarking is proposed in this paper, which improves the robustness of digital watermarking against geometric attacks by constructing stable geometric attack invari- ant features. In this paper, the Discrete Fourier Transform domain template watermark is used as the invariant feature against geometric attacks, and the embedding of the cyclic watermark is used to improve the watermark robustness for recovering the watermark synchroniza- tion relationship. To achieve blind extraction of the watermark, a parameter extraction method based on noise extraction is designed. The experimental results demonstrate that the proposed method can effectively improve the robustness of digital watermarking of remote sensing images against geometric attacks. Meanwhile, it can also resist common image processing attacks and compound attacks.

Cloud pollution on remote sensing images seriously affects the actual use rate of remote sensing images. Therefore, cloud detection of remote sensing images is an indispensable part of image preprocessing and image availability screening. Aiming at the lack of short wave infrared and thermal infrared bands in ZY-3 high-resolution satellite images resulting in the poor detection effect, considering the obvious difference in geographic height between cloud and ground surface objects, this paper proposes a thick and thin cloud detection method combining spectral information and digital height model (DHM) based on multi-scale features-convolutional neural network (MF-CNN) model. To verify the importance of DHM height information in cloud detection of ZY-3 multi-angle remote sensing images, this paper implements cloud detection comparison of the data set with and without DHM height information based on the MF-CNN model. The experimental results show that the ZY-3 multi-angle image with DHM height information can effectively improve the confusion between highlighted surface and thin cloud, which also means the assistance of DHM height information can make up for the disadvantage of high-resolution image lacking short wave infrared and thermal infrared bands.

The purpose of this paper is to demonstrate how geospatial technologies, especially remote sensing, can play a leading role in defining urban sustainability based on the evaluation of demand and supply of ecosystem services (ES ). A brief review of sustainable development and urban sustainability will be given followed by demonstrations of the need for green spaces in cities, and the consequences of fragmentation of green spaces on biodiversity. Although there are no substantive figures for desirable levels of green spaces in urban areas for the benefit of inhabitants, the paper proposes minimum desirable areal percentages. The paper defines natural capital and ES and the procedures adopted by researchers in balancing the supply and demand for ES for urban areas. The genuine progress indicator is presented as a measure of assessing human welfare, but it is not pursued as an indicator of sustainability. Examples of the applications of remote sensing technologies for determining supply and demand of ES are reviewed as are the potential of the supply and demand of ES to support decision-making in urban areas, to ensure that development decisions are sustainable and are in the best interests of the urban residents who depend on ES for their life support.

In this paper, the frequency shift system calibration of the wind imaging interferometer is analyzed. By establishing the frequency shift system vibration and reflectivity models, the single factor and comprehensive factors models are used to invert the wind speed and temperature, respectively. The parameters of the frequency shift system that meet the design accuracy requirement of the instrument are determined. The conclusion of this paper provides theoretical instructions for the calibration process of wind imaging interferometer.

The Gao Fen Xiang Ji (GFXJ) is the first Chinese self–developed airborne three–line array charge-coupled devices (CCD) camera and is designed to meet 8 cm ground sample distance (GSD), 0.5 m planimetry accuracy, and 0.28 m elevation accuracy for ground three-dimensional (3D) points at a flight height of 2000 m. These values also meet the 1:1000 scale mapping requirements in China. However, the original direct geopositioning accuracy of the GFXJ is approximately 4 m in the planimetry direction and 6 m in the elevation direction. To meet the ground 3D point accuracy requirements and improve the direct geopositioning accuracy of the GFXJ, this paper carries out a deep investigation on the GFXJ geometric calibration. This geometric calibration includes two main parts: the Global Navigation Satellite System (GNSS) lever arms and inertial measurement unit (IMU) boresight misalignment calibration, and the camera lens and CCD line distortion calibration. First, a brief introduction is given on the imaging properties of the GFXJ camera. Then, the GNSS lever arms and IMU boresight misalignment calibration models are built for the GFXJ camera. Next, a piecewise self-calibration model based on the CCD viewing angle is established for the GFXJ lens and CCD line distortion calibration. Subsequently, an iterative two-step calibration scheme is proposed for the geometric calibration. Finally, experiments were implemented using multiple flight blocks obtained in the Songshan remote sensing comprehensive field and the Hegang area of Heilongjiang Province. Through calibration experiments, geometric calibration values were obtained for the GNSS lever arms and IMU boresight misalignment. Reliable CAM files were independently generated for the forward, nadir, and backward line arrays. The experiments showed that the proposed GNSS lever arms and IMU boresight misalignment calibration models and the piecewise self-calibration model had good applicability and effectiveness for the GFXJ camera. The proposed two-step calibration scheme can significantly enhance the geometric positioning accuracy of the GFXJ camera. The original direct geopositioning accuracy of the GFXJ is approximately 4 m in the planimetry direction and 6 m in the elevation direction. Using the GNSS lever arms and the IMU boresight misalignment calibration values and the CAM files, the positioning accuracy of the GFXJ camera can fulfill the 3D point accuracy requirements and the 1:1000 mapping accuracy requirements at a 2000 m flight height after aerial triangulation with only several ground control points. The planimetry accuracy is approximately 0.2 m, and the elevation accuracy is less than 0.28 m. In addition, the calibration models and calibration scheme established in this paper can provide a reference for calibration studies on other airborne linear array CCD cameras.

In this paper we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labeled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labeling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-) data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains eight semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields, and castles. We describe our labeling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labeled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D.net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general.

Satellite images from multiple sources with different resolutions are currently able to observe the same region. Reliable image matching between these images is the first step in their integrated use. Image matching of multiple-resolution images is not trivial because of the large geometric differences among the images, which can cause failure of matching and losses of matching accuracy. This paper presents a bound-constrained, multiple-image, least-squares matching (LSM) method that extends the classical LSM in two ways for better performance. First, the a priori metadata of the images, including the geo-referencing and scale information, are used for initial matching and to provide bound constraints in the LSM to improve its stability. Second, multiple images are matched in a single optimization rather than the traditional pairwise matching. This brings additional observations in the least-squares optimization, which makes the matching aware of both larger and local context and improves matching quality even with inaccurate initializations for high resolution images. Experimental analysis using multiple-source satellite images with multiple resolutions collected on Mars and in Hong Kong reveals that the proposed method is capable of obtaining reliable multiple-fold matches effectively, even in challenging cases with resolution differences as much as 20-fold. The method proposed in this paper has significance for the synergistic use of multiple-source satellite images in various applications.

The detection of changes in synthetic aperture radar (SAR) images based on deep learning has been widely used in landslides detection, flood disaster monitoring, and other fields of change detection due to its high classification accuracy. However, the inherent speckle noise in SAR images restricts the performance of existing SAR image change detection algorithms by clustering analysis. Therefore, this paper proposes a novel method for SAR image change detection based on clustering fusion and deep neural networks. We first used hierarchical fuzzy c-means clustering (HFCM ) to process two different images to obtain HFCM classification results. Then a fusion strategy is designed to obtain the fused image from the two HFCM classified images as the pre-classification result. Furthermore, a lightweight deep neural network com posed of a decomposition convolution module and an auxiliary classification module was proposed; the former module could reduce network parameters by 28%, and the latter could reduce network parameters by 33.3%. To improve the recognition performance of the network, the classification layer was replaced by the regression layer at the outcome of the network. By comparing the experiments of different methods on five data sets, the performance of our proposed method is superior.

In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.

Although most deep learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification, they are often limited by complex networks and require massive training samples in practical applications. Therefore, designing an efficient, lightweight model to obtain better classification results under small samples situations remains a challenging task. To alleviate this problem, a novel, lightweight parallel octave convolutional neural network (LPOCNN) for HSI classification is proposed in this paper. First, the HSI data is preprocessed to construct two three-dimensional (3D) patch cubes with different spatial and spectral scales for each central pixel, removing redundancy and focusing on extracting spatial features and spectral features, respectively. Next, two non- deep parallel branches are created for the two inputs, which design octave convolution rather than classical 3D convolution to facilitate light weighting of the model. Then two-dimensional convolutional neural network is used to extract deeper spectral-spatial features when fusing spectral-spatial features from different parallel layers. Moreover, the spectral-spatial attention is designed to promote the classification performance even further by adaptively adjusting the weights of different spectral-spatial features according to their contribution to classification. Experiments show that our suggested LPOCNN acquires a significant advantage on classification performance over other competitive methods under small sample situations.

The precise geo-positioning of high-resolution satellite imagery (HRSI) without ground control points (GCPs) is an important and fundamental step in global mapping, three-dimensional modeling, and so on. In this paper, to improve the efficiency of large-scale bundle adjustment (BA), we propose a combined Preconditioned Conjugate Gradient (PCG) and Graphic Processing Unit (GPU) parallel computing approach for the BA of large-scale HRSI without GCPs. The proposed approach consists of three main components: 1) construction of a BA model without GCPs ; 2) reduction of memory consumption using the Compressed Sparse Row sparse matrix format; and 3) improvement of the computational efficiency by the use of the combined PCG and GPU parallel computing method. The experimental results showed that the proposed method: 1) consumes less memory consumption compared to the conventional full matrix format method; 2) demonstrates higher computational efficiency than the single-core, Ceres-solver and multi-core central processing unit computing methods, with 9.48, 6.82, and 3.05 times faster than the above three methods, respectively; 3) obtains comparable BA accuracy with the above three methods, with image residuals of about 0.9 pixels; and 4) is superior to the parallel bundle adjustment method in the reprojection error.

Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i. e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE.

Most of the feature selection ( fs ) methods in the literature determine features that are appropriate only for a given dataset. In contrast, in this paper a FS method that is not dependent to a specific dataset is proposed. In this regard, the effective feature types based on reasonable facts are predefined and appropriate candidate features for each feature type are selected. In proposed method, the features selected from a single labeled image can be used in segmentation of images captured by different satellites with similar spatial resolution. The selected feature types contain spatial and spectral features. The selected features are applied for segmentation of the images captured by QuickBird and GeoEye satellites and obtained results of proposed method are compared with well-known FS methods. Using different evaluation measures, our comparison shows the efficiency of the proposed method in providing better segmentation compared to other FS methods that are presented in this paper.

In this paper a novel road vectorization methodology based on image space clustering technique and weighted graph theory is presented. The proposed methodology describes a road as a set of optimized points on the centerline which should be connected by defining a number of appropriate criteria. The main contribution of this paper is to design a weighting scheme for combining a small number of road identities using Ordered Weighted Averaging ( owa ) operators by defining appropriate decision strategy. In this regard, a novel geometric criterion is introduced. Result of the OWA aggregation specifies weight of each edge in the road network graph. Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters. Obtaining improved quality measures for several high-resolution images, demonstrates the successfulness of the vectorization approach.

This paper presents a novel strategy for object-based change detection using very high spatial resolution (VHR) satellite images captured under variable off-nadir view angles. The variable off-nadir angle, along with weak absolute orientation, generates spurious slivers during the multitemporal comparison of classification results. The proposed solution for accurate object-to-object comparison is based on an intermediate registration of object-based classification results with a piecewise affine transformation followed by robust, geometry-based techniques for sliver removal. Although different remote sensing applications require different strategies and methods for object-based change detection, the approach developed in this paper can overcome the overall limitation introduced by the slivers generated by weak geo-localization, variable off-nadir angles, and image segmentation.

This paper seeks to draw attention to the multiple comparison problem ( mcp ) within the remote sensing community, and suggest some easily implemented solutions. The use of repeated statistical tests by remote sensing scientists to identify significant relationships, increases the chance identifying false positives (i.e., type-I errors) as the number of tests increases. This paper provides an introduction to the multiple comparison problem (i.e., the impact of the interpretation of p-values when repeated tests are made), outlines some simple solutions, and provides two case studies to demonstrate the potential impact of the problem in empirical remote sensing. The first case study looks at multiple potential texture metrics to predict leaf area index. The second case study examines pixel-wise temporal trend detection. The results show how applying solutions to the multiple comparison problem can greatly impact the interpretation of statistical results.

Remote sensing scene images are characterized by intra-class diversity and inter-class similarity. When recognizing remote sensing images, traditional image classification algorithms based on deep learning only extract the global features of scene images, ignoring the important role of local key features in classification, which limits the ability of feature expression and restricts the improvement of classification accuracy. Therefore, this paper presents a multi-channel attention fusion network (MCAFNet). First, three channels are used to extract the features of the image. The channel "spatial attention module" is added after the maximum pooling layer of two channels to get the global and local key features of the image. The other channel uses the original model to extract the deep features of the image. Second, features extracted from different channels are effectively fused by the fusion module. Finally, an adaptive weight loss function is designed to automatically adjust the losses in different types of loss functions. Three challenging data sets, UC Merced Land-Use Dataset (UCM), Aerial Image Dataset (AID), and Northwestern Polytechnic University Dataset (NWPU), are selected for the experiment. Experimental results show that our algorithm can effectively recognize scenes and obtain competitive classification results.

Both UAV photogrammetry and lidar have become common in deriv- ing three-dimensional models of urban scenes, and each has its own advantages and disadvantages. However, the fusion of these multi- source data is still challenging, in which registration is one of the most important stages. In this paper, we propose a method of coarse point cloud registration which consists of two steps. The first step is to extract urban building facades in both an oblique photogram- metric point cloud and a lidar point cloud. The second step is to align the two point clouds using the extracted building facades. Object Vicinity Distribution Feature (Dijkman and Van Den Heuvel 2002) is introduced to describe the distribution of building facades and register the two heterologous point clouds. This method provides a good initial state for later refined registration process and is transla - tion, rotation, and scale invariant. Experiment results show that the accuracy of this proposed automatic registration method is equiva- lent to the accuracy of manual registration with control points.

It was difficult to accurately obtain crop planting structure by using the spectral information of high spatial resolution and low spatial resolution multispectral images of panchromatic images at the same time. In this paper, we propose a method of planting structure extraction based on indices and an improved U-Net semantic segmentation network. Based on the original band of Landsat-8, we used an image fusion algorithm to highlight the characteristics of vegetation, water, and soil respectively by three indices added, and the improved U-Net network was used to classify the type of planting structure. The results showed that the overall accuracy of classification was more than 91.6%, and the accuracy of crops was up to 93.8%. Automated water extraction index in image fusion effectively improved the classification accuracy. This method could extract a variety of information about planting structures automatically and accurately. It provided theoretical support for adjusting and optimizing regional planting structures.

Nowadays, the type of vehicles, either tracked or wheeled in the Army has increased considerably and every decision maker wants the current details of off-road trafficability for operation planning. Therefore, vehicle-specific trafficability maps are the need of the hour. Emphasis should be given on soils capable of bearing the moving load of a vehicle and it is an important factor to be considered. Soil variability in spatial and temporal dimensions affects the assessment of off-road trafficability. Genetically, it is assumed that similar soil types behave similarly at a regional scale to reduce the complexity due to its variability. Remolding Cone Index (RCI) of Soil is the indicator of its capability and for generic solution; its value can be related to gravimetric moisture of soil for getting a general idea. In this paper, a logics-based, new concept has been introduced to rationalize the RCI values of these moist areas. Most significantly, moisture- and water-bound areas play an important role in the assessment of off-road trafficability. Therefore, to cover larger areas, a grid-based approach was taken as a base and, to get a preliminary idea of prevailing moisture, Normalized Difference Water Index was also mapped. Every vehicle has fixed vehicle cone index based on its vehicular characteristics and can be related with RCI for trafficability purpose. This new technique will save time and field work and is immensely useful for the trafficability assessment of any specific vehicle.

The primary prerequisite for sustainable urban development is to accurately grasp the development of the city. The dynamic changes in the urban area can reflect the urban expansion process and spatial development model. Carrying out urban expansion monitoring and extracting urban areas is of great importance for grasping the law of urban development and promoting the sustainable development of cities. However, the related research reveals several problems such as insufficient accuracy and low intelligence of urban boundary extraction. In response to these problems, this paper proposes a new method for urban area extraction based on the progressive learning model. By combining prior knowledge and image features, the number of training samples required for machine learning is reduced, and the problem of using high-level semantic information expression in the process of urban areas is avoided by using the classification method, and thus the accuracy of urban area extraction is improved. The method uses urban road network data to divide the city into blocks. It applies the scene classification method to extract the urban areas and uses the pyramid layer-by-layer to carry out the space constraint method to integrate the urban extraction principle into the machine learning process, which can be obtained and kept artificial under a small sample condition. Extracting the effect of the urban area is very close, greatly reducing the workload and providing a new solution for high-precision automatic extraction of urban areas. Through the analysis of urban expansion, the following results were obtained: (1) from 2000 to 2015, China's provincial capital cities maintained a high-speed growth trend with a total area increased by 90%; (2) urban expansion showed significant regional differences. The eastern expansion rate gradually slowed down, the western and northeast regions accelerated their expansion, and the central region expanded steadily; (3) 61% of the provincial capital cities expanded exponentially; (4) the development of China's provincial capital cities was highly correlated with national urban development policies and regional development strategies.

Structure-from-motion (SfM) techniques have been widely used for three-dimensional (3D) scene reconstruction from sequential video frames. However, for reconstructing narrow and confined spaces such as the interior of drainage pipes, selecting geometrically optimal frames is a major challenge, not only to reduce the number of needed frames but also to yield better geometry. This paper introduces a coarse-to-fine method to optimize the selection of monocular video frames based on a geometric criterion called feature-based parallax analysis for 3D pipe reconstruction. The proposed method was applied in two experiments with a monocular camera fixed on a customized robot. Experimental results show that our approach only requires respectively 9.66% and 3.15% of the number of frames. The spatial distribution of the retrieved frames was uniform and reasonable, enabling the successful SfM process to achieve a complete reconstruction of the pipe geometry.

Geometric registration of multiple-source data is of great value for fusion processing and is very beneficial for the research of desert ecosystems. A lidar point cloud and optical image are two typical data that need to be integrated for data assimilation and information retrieval. This paper aims to solve the registration problem of aerial imagery and airborne lidar data in desert areas where traditional registration methods have difficulties in identifying registration primitives. In many deserts, such as the Sahara in Africa and Gobi in China, we observe that there are unevenly distributed desert bushes, which can be used as cues for registration. In this paper, we propose a registration approach using the centroids of bushes as registration primitives. This approach employs similar triangles created from both centroids as the evidence for matching and verifies the registration by the RANSAC algorithm. Experiments using data taken from the Dunhuang Gobi Desert in China show the registration surface model visually, and at the same time quantifies the deviation error, which corroborates that the proposed registration method is effective and feasible in desert areas.

The paper outlines a method to compare two digital surfaces of the same rock face to detect major changes resulting from detached rocks and deformations. A terrestrial laser scanning survey is used for data gathering. After georeferencing, if the cliff has a complex morphology, a 3D segmentation algorithm is applied to split the whole rock surface into more subregions with an almost planar structure. In each subregion the raw point cloud is resampled on a regular grid and multitemporal differences are analyzed. Anomalies in differences, which should be very close to zero if no geometric variations have occurred, are identified with the following purposes: (a) localizing gross changes due to rock detachments, (b) removing global rigid-body displacements, and (c) understanding local cliff deformations. In the case where the rock face is covered by vegetation, this has to be filtered out, e.g., by visual inspection of RGB images co-registered to the point cloud. This paper also describes a procedure to carry out vegetation filtering in automatic way from the analysis of near-infrared images captured by a camera integrated to laser scanner. The application of the full processing pipeline has been tested on a real case study located in the Italian pre-alpine area. Here, after filtering some vegetation, a total rock fall volume of 0.15 m3 was detected on a cliff of about 375 m2 and within a period of six months.

This paper presents a cell-based approach for computing the deformation of a monitored object by analyzing point cloud data from terrestrial lidar. This approach can automatically generate an informative deformation description (called "deformation map") with distinctive deformation characteristics for different partial areas. The approach consists of three major computing steps: (a) "split" - the space of the monitored object is divided into 3D uniform cells, (b) "detect" - deformation parameters for each cell (called "meta-deformation") are estimated by comparing the point clouds in the cell sampled at Epochs I and II, and (c) "merge" - the adjacent cells with similar meta-deformation are combined together in a partial area with a consistent "sub-deformation." The main contributions of this paper ore: (a) a hybrid deformation model for incremental and comprehensive deformation representation, including meta-deformation, sub-deformation and deformation map, (b) a systematic procedure of "split-detect-merge" to automatically and gradually estimate the hybrid deformation model, and (c) a complete validation with a couple of synthetic and real datasets.

Vehicle-borne laser-scanned point clouds have become increasingly important 3D data sources in fields such as digital city modeling and emergency response management. Aiming at reducing the technical bottlenecks of management and visualization of very large point cloud data sets, this paper proposes a new spatial organization method called 3DOR-Tree, which integrates Octree and 3D R-Tree data structures. This method utilizes Octree's rapid convergence to generate R - T ree leaf nodes, which are inserted directly into the R - T ree, thus avoiding time-consuming point-by-point insertion operations. Furthermore, this paper extends the R - T ree structure to support LOD (level of detail) models. Based on the extended structure, a practical data management method is presented. Finally, an adaptive control method for LODS of point clouds is illustrated. Typical experimental results show that our method possesses quasi-real-time index construction speed, a good storage utilization rate, and efficient visualization performance.

The paper provides an account of how three key relief organizations worked together after the devastating Haiti earthquake to produce the first damage assessment based mainly on the use of remotely-sensed imagery. This assessment was jointly conducted by the World Bank (WB), the United Nations Institute for Training and Research (UNITAR) Operational Satellite Applications Programme (UNOSAT), and the European Commission’s Joint Research Centre (JRC). This paper discusses the data sources used for the assessment, the methodologies employed to evaluate building damage, and a set of independent studies to validate the final damage results. Finally, a vision of the role of remote sensing technologies in future disasters is presented that serves as a road map for methodological improvements.

We present a model for the alignment of image sets using topographic data as control information. Traditional photogrammetric practices are point-based and require well-measured and clearly marked entities within the scene; for repeated measurement, their demarcation should also be durable. Nonetheless, placement of photogrammetric control points oftentimes cannot be readily implemented within the area for documentation. In many of those cases, some form of topographic model, either coarse or fine, exists or can be readily established. This topographic description provides a broad and markerless coverage of control information, which, if modeled appropriately, can substitute the use of a control-point network in the alignment process. We present in this paper a registration model for terrestrial images based on surfaces as control information. The paper addresses both the problem of acquiring mutual corresponding entities and registration based on varying resolutions. The application of the proposed approach is demonstrated on a mapping of an archeological site and for registration of a set of terrestrial images of an open, unstructured scene using airborne laser scanning data. Results show both the applicability of such a model and that estimation comparable in quality to point based observations can be achieved.

This paper is focused on the potential and limits of Persistent Scatterer Interferometry (PSI), a powerful remote sensing technique used to measure deformation phenomena. It only refers to satellite-based PSI techniques, focusing on the most important sources of C-band SAR data: ERS and Envisat. In addition, it compares C- and X-band results, considering data from the high-resolution TerraSAR-X sensor. The paper begins with a description of the main characteristics of PSI. It then discusses the most important PSI products and their performances, analyzing their spatial sampling, the so-called residual topographic error and PSI geocoding, the average displacement rates, and the deformation time series. As C-band products are concerned, the paper reports some relevant PSI validation results, which come from the ESA-funded Terrafirma Validation Project. Regarding the X-band, it describes the results obtained over the City of Barcelona by processing 13 TerraSAR-X images. The last part discusses the main limits of PSI.

Multi-Layer Perceptrons (MLPS) are powerful function approximators. In the last decades they were successfully applied to many different regression and classification problems. Their characteristics and convergence properties are well studied and relatively well understood, but they were originally designed to work with real-valued data. The main focus of this paper is the classification of polarimetric synthetic aperture radar (POLSAR) data which are a complex-valued signal. Instead of using an arbitrarily projection of this complex-valued data to the real domain, the paper proposes the usage of complex-valued MLPS (CV-MLPS), which are an extension of MLPS to the complex domain. The paper provides a detailed yet general derivation of the complex backpropagation algorithm and mentions related problems as well as possible solutions. Furthermore, it evaluates the performance of CV-MLPS in a land-cover classification task in POLSAR images under several learning conditions, and compares the proposed classifier with standard methods. The experimental results show that CV-MLPS are successfully applicable to classification tasks in POLSAR data. They show good convergence properties and a better performance if compared to real-valued MLPS.

Within this paper, results of a test on the evaluation of the most recent photogrammetric digital airborne camera systems are presented. This project was initiated by the German Society of Photogrammetry, Remote Sensing and Geoinformation (DGPF), based on data from 12 different digital sensor systems flown in a well-controlled test site. The main topics of the evaluation phase are the analysis of geometric accuracy and sensor calibration, the radiometric performance including on-site radiometric calibration and multi-spectral land classifications, the performance of photogrammetric Digital Surface Model (DSM) generation, and the potential of manual stereoplotting from digital images. Within this paper, only results obtained at the Institute of Photogrammetry (IFP), Universitaet Stuttgart will be presented, comprehensively proving the high performance of digital camera systems and processing. The IFP served as the pilot center and additionally concentrated on the investigation of camera geometry and the image based generation of height data.

This paper describes an optimized implementation of an object-based cellular automata (CA) model recently developed to overcome the sensitivity of standard raster CA models to cell size and neighborhood configuration. In this CA model, space is partitioned using a vector structure in which the polygons correspond to meaningful geographical entities. The model allows the geometric transformation of each object based on the influence of its respective neighbors. In addition, it incorporates the concept of dynamic neighborhood where the neighborhood relationships among objects are expressed semantically, removing any restriction of distance in the neighborhood definition. The optimized implementation described in this paper makes use of a spatial database and spatial indexes to handle several spatial operations, which considerably reduces the computation time required for the simulations. The model is simple, flexible and robust, and can be easily adapted to various geographic areas at different scales.

Remotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression.

Lidar systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality assurance of lidar systems and quality control of the derived data has not followed the same trend. In other words, a lack of reliable, practical, cost-effective, and commonly-acceptable methods for quality evaluation is evident. A frequently adopted procedure for quality evaluation is the comparison between lidar data and ground control points. Besides being expensive, this approach is not accurate enough for the verification of the horizontal accuracy, which is known to be worse than the vertical accuracy. This paper is dedicated to providing an accurate, economical, and convenient quality control methodology for the evaluation of lidar data. The paper starts with a brief discussion of the lidar mathematical model, which is followed by an analysis of possible random and systematic errors and their impact on the resulting surface. Based on the discussion of error sources and their impact, a tool for evaluating the quality of the derived surface is proposed. In addition to the verification of the data quality, the proposed method can be used for evaluating the system parameters and measurements. Experimental results from simulated and real data demonstrate the feasibility of the proposed tool.

Geometric sensor models are used in image processing to model the relationship between object space and image space and to transform image data to conform to a map projection. An Rational Polynomial Coefficient (RCP) is a generic sensor model that can be used to transform images from a variety of different high resolution satellite and airborne remote sensing systems. To date, numerous researchers have published papers about RPC refinement, aimed at improving the accuracy of the results. So far, the Bias Compensation method is the one that is the most accepted and widely used, but this method has rigorous conditions that limit its use; namely, it can only be used to improve the RPC of images obtained from cameras with a narrow field of view and small attitude errors, such as those used on Ikonos or QuickBird satellites. In many cases, these rigorous conditions may not be satisfied (e.g., cameras with a wide field of view and some satellites with large ephemeris and attitude errors). Therefore, a more robust method that can be used to refine the RPC under a wider range of conditions is desirable. In this paper, a generic method for RPC refinement is proposed. The method first restores the sensor's pseudo position and attitude, then adjusts these parameters using ground control points. Finally a new RPC is generated based on the sensor's adjusted position and attitude. We commence our paper with a review of the previous ten years of research directed toward RPC refinement, and compare the characteristics of different refinement methods that have been proposed to date. We then present a methodology for a proposed generic method for RPC refinement and describe the results of two sets of experiments that compare the proposed Generic method with the Bias Compensation method. The results confirm that the Bias Compensation method works well only when the aforementioned rigorous conditions are met. The accuracy of the RPC refined by the Bias Compensation method decreased rapidly with the sensor's position error and attitude error. In contrast to this, the Generic method proposed in this paper was found to yield highly accurate results under a variety of different sensor positions and attitudes.

This paper presents a method to compare maps that contain pixels that have partial membership to multiple categories, i.e., mixed or soft classified pixels. The method quantifies ranges for associations among categories based upon possible variations in sub-pixel spatial allocation. The paper derives the mathematical equations for constructing the range of associations based on three types of cross-tabulation matrices, the greatest matrix, the random matrix, and the least matrix. We demonstrate how the analysis can be combined with multiple resolution map comparison to specify the resolution at which clusters exist on a single map or between two maps. The method produces a range that reflects the amount of uncertainty in the categorical associations. We illustrate the procedure with both a simple example and data from the Plum Island Ecosystems study site in Massachusetts, USA.

Hemispheric image processing with the goal of solar radiation determination from ground-based fisheye images is a valuable tool for silvicultural analysis in forest ecosystems. The basic idea of the technique is taking a hemispheric crown image with a camera equipped with a 180° fisheye lens, segmenting the image in order to identify solar radiation relevant open sky areas, and then merging the open sky area with a radiation and sun-path model in order to compute the total annual or seasonal solar radiation for a plant. The results of hemispheric image processing can be used to quantitatively evaluate the growth chances of ground vegetation (e.g., tree regeneration) in forest ecosystems. This paper shows steps towards the operationalization and optimization of the method. As a prerequisite to support geometric handling and georeferencing of hemispheric images, an equi-angular camera model is shown to describe the imaging geometry of fisheye lenses. The model is extended by a set of additional parameters to handle deviations from the ideal model. In practical tests, a precision potential of 0.1 pixels could be obtained with off-the-shelf fisheye lenses. In addition, a method for handling the effects of chromatic aberration, which may amount to several pixels in fisheye lens systems, is discussed. The central topic of the paper is the development of a versatile method for segmenting hemispheric forest crown images. The method is based on linear segmentoriented classification on radial profiles. It combines global thresholding techniques with local image analysis to ensure a reliable segmentation in different types of forest under various cloud conditions. Sub-pixel classification is incorporated to optimize the accuracy of the method. The performance of the developed method is validated in a number of practical tests.

The objective of this paper is to examine the scaling-up effect on the relationship between landscape patterns and land surface temperatures based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four TERRA ASTER images were acquired to derive the land-use and land-cover (LULC) patterns and land surface temperatures (LST) in different seasons. Each LULC and LST image was resampled to eight spatial scales: 15, 30, 60, 90, 120, 250, 500, and 1,000 m. The scaling-up effect on the spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. Optimal spatial resolutions were determined on the basis of the minimum distance in the landscape metric spaces. The results show that the patch percentages of LULC and LST patches were not strongly affected by the scaling-up process in different seasons. The patch densities and landscape shape indices and LST patches kept decreasing across the scales without distinct seasonal differences. Thirty meters was found to be the optimal resolution in the study of the relationship between urban LULC and LST classes. Ninety meters was found to be the optimal spatial resolution for assessing the landscape-level relationship between LULC and LST patterns. This paper may provide useful information for urban planners and environmental practitioners to manage urban landscapes and urban thermal environments as a result of urbanization.

This paper discusses the calibration and assessment of a cellular automata model for urban growth modeling. A number of transition rules are introduced in the cellular automata model to consider the most influential urbanization factors, such as land-cover maps obtained from satellite images and population density from the census. The transition rules are calibrated both spatially and temporally to ensure the modeling accuracy. Spatially, each township (about 6 miles 3 6 miles) in the study area is used as a calibration unit such that the spatial variability of the urban growth process can be taken into account. The temporal calibration is performed by using a sequence of remote sensing images from which the land-cover information at different years is extracted. As for the assessment, fitness (for urban level match) and two types of modeling errors (for urban pattern match) are introduced as the evaluation criteria. The study shows that the use of images reduces the need for a large number of input data. Evaluation on the rule variogram reveals that the transition rule values are correlated spatially and vary with the urbanization level. The paper reports the study outcome over the city of Indianapolis, Indiana for the past three decades using Landsat images and the population data.

During the past several years, point density covering topographic objects with airborne lidar (Light Detection And Ranging) technology has been greatly improved. This achievement provides an improved ability for reconstructing more complicated building roof structures; more specifically, those comprising various model primitives horizontally and/or vertically. However, the technology for automatically reconstructing such a complicated structure is thus far poorly understood and is currently based on employing a limited number of pre-specified building primitives. This paper addresses this limitation by introducing a new technique of modeling 3D building objects using a data-driven approach whereby densely collecting low-level modeling cues from lidar data are used in the modeling process. The core of the proposed method is to globally reconstruct geometric topology between adjacent linear features by adopting a BSP (Binary Space Partitioning) tree. The proposed algorithm consists of four steps: (a) detecting individual buildings from lidar data, (b) clustering laser points by height and planar similarity, (c) extracting rectilinear lines, and (d) planar partitioning and merging for the generation of polyhedral models. This paper demonstrates the efficacy of the algorithm for creating complex models of building rooftops in 3D space from airborne lidar data.

Most current digital photogrammetric workstations are based on feature points. Curved features are quite difficult to be modeled because they cannot be treated as feature points. The focus of the paper is on the photogrammetric modeling of space linear features. In general, lines and curves can be represented by a series of connected points, so called, generalized points in the paper. Different from all existing models, only one collinearity equation is used for each point on the linear curve, which makes the mathematical model very simple. Hereby, the key of generalized point photogrammetry is that all kinds of features are treated as generalized points to use either x or y collinearity equation. A significant difference between generalized point photogrammetry and conventional point photogrammetry is that image features are not necessarily exact conjugates. The exact conjugacy between image features and/or the correspondence between space and image feature are established during bundle block adjustment. Photogrammetric modeling of several space linear features is discussed. Sub-pixel precision has been achieved for both exterior orientation and 3D modeling of linear features, which verifies the correctness and effectiveness of the proposed approach.

Due to the existence of shadows, especially in urban environments, it is difficult to extract semantic information from aerial and high-resolution satellite images. In this paper, an efficient method of correcting shadow effects using multisource data sets in aerial color images is proposed. The proposed method has three steps. First, it accurately detects the shadowed regions using the image geometry and the solar position of the image acquisition data. Then, the detected shadowed regions are segmented according to land surface type. Finally, the shadow effects of the segmented regions are corrected by directly comparing the same nonshadow features with the segmented shadows. In the application part of this paper, the proposed techniques were applied in the extraction of an asphalt road from an image.

The Hellenic Cadastre Program (HCP) of Greece aims at developing a modern cadastral system for the first time in the Hellenic history. This paper is focused on the issues related to cadastral forestlands digital mapping, an indispensable part of HCP. Mapping the forestlands is a challenge of multiple disciplines. It includes photogrammetry, photointerpretation, Geographic Information Systems (GIS), and a clear understanding of the current institutional and legislative setting. The process requires both historical and current information pertaining to land-cover in order to identify forestland changes over time. Historical and current digital orthoimagery is generated through photogrammetric operations. Forestlands are delineated in a spatiotemporal environment; state property rights in forestlands are allocated and land ownership is established within the framework of HCP. This paper demonstrates that the integration of airborne remote sensing and collateral data with a GIS is an effective approach for cadastral forest mapping. The produced GIS databases and large-scale Forest Maps may serve as a data foundation towards a land register of forests.

This paper proposes a technique to extract urban built-up land features from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking two cities in southeastern China as examples. The study selected three indices, Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI) to represent three major urban land-use classes, built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bands derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through a supervised classification, a principal components analysis, or a logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of SAVI over NDVI and MNDWI over NDWI in the urban study are also discussed in this paper.

This paper summarizes a research project that examined the usefulness of Object-oriented Programming (OOP) and the Unified Modelling Language (UML) in producing well-designed software. This leads to cost effective implementation of new automated mapping tools as they emerge. In this project, software for an automated mapping system was designed, implemented, and successfully tested. The system consists of the following subsystems: 1) image acquisition, 2) positioning, 3) image point referencing, 4) spatial object extraction, and, 5) visualization. This paper addresses parts of this work (the first 3 subsystems) in order to demonstrate the flexibility of this approach to software design.

There are three strategies by which the accuracy of classification can be improved after the imagery that will be used for the classification has been chosen. These are to improve the definition of the class decision surfaces, to maximize the between class distances, and to reduce the within class variances. This paper reports on work done to investigate the relationship between classification accuracy and within class variances, where generally accepted measures of accuracy derived from the Confusion Matrix are used as the indicators of classification accuracy. This paper shows that the within class variances are a function of image resolution, and it provides a mechanism based on the Average Local Variance (ALV) function to find the resolution that will yield the highest relative within field classification accuracy by minimizing the within class variances.

In this paper, we evaluate the impacts of land-use/cover changes (LUCC) on sediment loads at the outlets of five sub-watersheds of the Poyang Lake watershed by integrating remote sensing and CIS with statistical analysis. The intensively farmed watershed is characterized by a mountainous and hilly topography and a rainy climate. The primary goal of this paper is to help a better understanding of land-use/cover change and its driving forces. We discuss spatiotemporal variations in rainfall and sediment loads and identify factors contributing to those variations, analyze the comprehensive impacts of land-use/cover change on changing climate and human activities, and conclude that the changing rates of forest cover and climate regimes are primary factors for sediment discharges in the Poyang Lake watershed. Our results suggest that the eco-system still have large capacities to support human activities in the area.