Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Climate change 2014 - Synthesis Report

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

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Do we need hundreds of classifiers to solve real world classification problems

A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

https://www.aboutgis.com/Publications/Mountrakis_SVM_review_in_remote_sensing_ISPRS2010.pdf

Support vector machines in remote sensing: A review

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Identification of dead trees using small footprint full-waveform airborne laser scanning data

After a short introduction into the Airborne Laser Scanning (ALS), the sampling process and a brief comparison of ALS with other data acquisition methods, the paper focuses on the generation of high quality digital terrain models from ALS data. In the first part the hierarchic robust interpolation method for the automatic filtering and classification, respectively, of an ALS point cloud into terrain and off-terrain (points on buildings or on the vegetation, etc.) is presented. Then, an approach for the modelling of linear features, such as break lines, based on the ALS data is described. Based on the feature lines and the classified terrain points a high quality DTM can be determined. For many subsequent processes the data reduction of the detailed DTM is essential in order to handle the large amount of data. Therefore, the paper presents a data reduction method that guaranties a certain DTM representation quality. Next to the presented algorithms, for each of the mentioned topics, references to other published methods are provided. Furthermore, beside the description of the algorithms, practical examples demonstrate the results of the algorithms. At the end of the paper, an outlook section discusses the advanced possibilities of DTM determination based on full-waveform ALS data.

Airborne laser scanning – high quality digital terrain modelling

. Usually aerial imagery are acquired through perspective projection which results in relief displacement in the map. Therefore, orthophotos from large scale aerial images over urban area often suffer from double mappings caused by sudden elevation changes in the scene. In the generation of true orthophotos, one aims at removing these perspective displacements and occlusions. Hence, knowledge of the surface shape is needed, as it has great impacts both on the orthorectification and occlusion identification. Mostly, the relief displacements are caused by buildings. In this paper, we present a method for the generation of true orthophotos from overlapping aerial images of known orientation, especially in built-up areas. Apart from the aerial images, the method does not depend on additional data sources. In particular, we focus on the derivation of building roof outline model using image segmentation and edge detection techniques under the guide of the classification result of point cloud from image matching . Experiment results on real data show the feasibility and the performance of the method.

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True orthophoto generation using multi-view aerial images

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Robust characterization of forest canopy structure types using full-waveform airborne laser scanning

. Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

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Norbert Pfeifer

Papers

Identification of dead trees using small footprint full-waveform airborne laser scanning data

Airborne laser scanning – high quality digital terrain modelling

True orthophoto generation using multi-view aerial images

Robust characterization of forest canopy structure types using full-waveform airborne laser scanning

a Comparison of Uav and Tls Data for Soil Roughness Assessment