Aurora Cuartero

Error Analysis of Terrestrial Laser Scanning Data by Means of Spherical Statistics and 3D Graphs

This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics.

Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images.

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Positional Accuracy Analysis of Satellite Imagery by Circular Statistics

The proposed method in this paper uses circular statistics for the analysis of errors in the positional accuracy of geometric corrections satellite images using Independent Check Lines (ICL) instead of Independent Check Points (ICP). Circular statistics has been preferred because of the vectorial nature of the spatial error. A study case has been presented and discussed in detail. From the TERRA-ASTER images of Extremadura area (Spain), the Ground Control Point (GCP), ICP, and ICL data were acquired using differential GPS through field survey, and the planimetric positional accuracy was analyzed by both the conventional method (using ICP) and the proposed method (using 1CL). Comparing conventional and proposed methods, the results indicated that modulus statistics are similar (e.g., RMSE of Geometric Correction 1 were 17.5 for the conventional method and 17.2 m for proposed method). But as additional results, azimuthal component statistics was calculated (e.g., mean direction: 247.2° in Geometric Correction 1), and several tests were made which showed the error distribution are not uniform and normal.

Methodological Proposal for Multispectral Stereo Matching

Remote sensors have begun to capture digital stereoscopic data. Although still monospectral (usually panchromatic), the capture of multispectral or hyperspectral stereoscopic data is just a matter of time. Digital photogrammetric workstations use area-based stereo-matching techniques based on the Pearson (product-moment) correlation coefficient. This is a technique that is not intended to take advantage of the multispectral data. The authors propose a new method that 1) can handle this multispectral information and 2) can take advantage of the spatial relations between pixel locations. The method is based on multidimensional scaling and Procrustes analysis. Our results indicate that the proposed new technique renders more robust results than classical methodology when noise in the original data is introduced

VecStatGraphs2D, A Tool for the Analysis of Two-Dimensional Vector Data: An Example Using QuikSCAT Ocean Winds

Circular or directional data are used in disciplines such as meteorology, geomatics, biology, and geology. The analysis of angular data requires special methods that are available in some statistical packages. However, these tools analyze only the angular values and do not include the vector modules, assuming unit vectors in all cases. In this letter, an open-source graphic and statistical package, i.e., VecStatGraphs2D, is described. It works in the R environment and provides statistics and graphics for modules (linear) and azimuths (circular), as well as graphics for the joint analysis of modules and azimuths. QuikSCAT satellite wind data are used to demonstrate some features of the package. QuikSCAT data are non-unit-length vectors, where both azimuth and magnitude (speed) are derived from u and v vector components (vector projections over the x- and y-axes). The example is used to show the seasonal change of winds in the Intertropical Convergence Zone, a key area in the ocean bird migration from the North to South Atlantic oceans.

Spectral partitioning for hyperspectral remote sensing image classification

In this paper, we present a new approach for spectral partitioning which is intended to deal with ill-posed problems in hyperspectral image classification. First, we use adaptive affinity propagation (AAP) to intelligently group the original spectral bands. Such grouping strategy not only allows us to reduce the number of spectral bands, but also to provide a different perspective on the original hyperspectral data. Then, a multiple classifier system (MCS) based on multinomial logistic regression (MLR) is applied. The system is trained using different band subsets resulting from the previously conducted intelligent grouping, and the results are combined to produce a final classification result. Our experimental results, conducted using the well-known hyperspectral scenes collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in NW Indiana, indicate that the proposed method can provide important advantages in terms of classification, in particular, when the number of training samples available a priori is very low.

The Use of Spherical Statistics to Analyze Digital Elevation Models: An Example From LIDAR and ASTER GDEM

The use of spherical statistics for comparing and analyzing different digital elevation models (DEMs) is proposed in this letter by the new software package VecStatGraphs3D. Spherical data deal with angular data such as unit vector in 3-D space and are used in disciplines such as meteorology, geomatics, biology, and geology. Usually, the most common descriptor to analyze DEMs is the root-mean-square error (linear statistic), but this statistic itself cannot describe the error positional distributions. Comparing homologous points in two different DEMs generates 3-D vectors, which can be analyzed by means of spherical statistics. A 3-D vector is defined by its module (linear statistics) and colatitude and longitude (spherical statistics). While several graphical and statistical tools are available for the analysis of directional data in 3-D, these tools consider only the angular magnitude and work with unit vectors. In this letter, the open-source software package VecStatGraphs3D is described. It works in the R environment and provides statistics for modules (linear), colatitude and longitude (spherical), and graphics for the joint analysis of 3-D vectors. The spatial locations of singular points between two DEMs of different spatial resolution: LIDAR (5 m) and ASTER Global Digital Elevation Map (30 m) are compared as examples using VecStatGraphs3D. The interactive 3-D graphics reveal spatial patterns and assist in understanding the effect of the DEM resolution on the uncertainty of the spatial locations of relief.

Fusion of hyperspectral and lidar data using generalized composite kernels: A case study in Extremadura, Spain

The light detection and ranging (LiDAR) data provides very valuable information about the height of the surveyed area which can be used as a source of complementary information for the classification of hyperspectral data, in particular when it is difficult to separate complex classes. In this work, we suggest to exploit the generalized composite kernel strategy for fusion and classification of hyperspectral and LiDAR data. Our experimental results, conducted using a hyperspectral image and a LiDAR derived intensity image collected over a rural area in Extremadura, Spain, indicate that the proposed framework for the fusion of hyperspactral and LiDAR data provides significant classification results.

Hyperspectral and lidar data integration and classification

Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to 1-5 meters per pixel. In this work, we propose to integrate both hyperspectral and LiDAR data by adding the LiDAR information to the hyperspectral data cube and correcting the geometric distortions. After arranging both data sets in the same format, we analyzed the errors obtained for each data source in order to determine if the final resolution adopted was the most appropriate one for performing data fusion. Our experimental results, in an area of Extremadura, indicate improvements in the classification after integrating the hyperspectral and LiDAR data.