Allan Aasbjerg Nielsen

Technical University of Denmark | DTU · DTU Compute - Applied Mathematics and Computer Science

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hh</sub> S ∗ <s...

This paper describes the latest developments in our work on complex Wishart distribution-based detection of change in time series of multi-look polarimetric synthetic aperture radar data in the covariance matrix representation. These developments include better approximations of the probability measures associated with the omnibus test statistics Q...

The increased amount of information measured by fully polarimetric SAR give additional knowledge about ground scatterers. Making the best use of the polarimetric information is crucial for target detection, amongst other applications. Several representations of the data, such as polarimetric decompositions, have been proposed to summarize the infor...

Cet ouvrage traite des avancées en analyse des séries chronologiques d’images de télédétection par apprentissages statistique, automatique et/ou profond. Il présente un éventail de modèles mathématiques, de méthodes d’extraction d’informations spatio-temporelles et d’applications en observation de la Terre.Détection de changements et analyse des sé...

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms ${\langle \boldsymbol{S}_{hh}\boldsymbol{S}_{hv}^*\rangle }$ , ${\langle \boldsymbol{S}_{vv}\boldsymbol{S}_{hv...

This chapter considers the change detection problem in a time series of polarimetric synthetic aperture radar (SAR) images using the covariance representation of multilook polarimetric SAR data. The change detection pipeline consists of an omnibus test for testing equality over the whole time span and a subsequent factorization used in assessing in...

This paper explores the similarities of output layers in Neural Networks (NNs) with logistic regression to explain importance of inputs by Z-scores. The network analyzed, a network for fusion of Synthetic Aperture Radar (SAR) and Microwave Radiometry (MWR) data, is applied to prediction of arctic sea ice. With the analysis the importance of MWR rel...

In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Frac...

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance i...

Temporal filtering for speckle reduction of polarimetric SARimages is described. The method is based on a sequential complex Wishart-based change detection algorithm which is applied to polarized SAR imagery, including the dual-polarization intensity data archived on the Google Earth Engine (GEE). Software for convenient application and analysis is...

With a growing number of different satellite sensors, data fusion offers great potential in many applications. In this work, a convolutional neural network (CNN) architecture is presented for fusing Sentinel-1 synthetic aperture radar (SAR) imagery and the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The CNN is applied to the prediction o...

Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API fo...

Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is comm...

We describe the calculation of eigenvalues of 2 x 2 or 3 x 3 Hermitian matrices as used in the analysis of multilook polarimetric synthetic aperture radar (SAR) data. The eigenvalues are calculated as the roots of quadratic or cubic equations. We also describe the pivot-based calculation of the Loewner order for the partial ordering of differences...

ABSTRACT Today, ice charts in Greenland waters are produced manually by the Danish Meteorological Institute (DMI) for selected regions depending on season and shipping routes. The project “Automated Downstream Sea Ice Products for Greenland Waters” or shorter “Automated Sea Ice Products” (ASIP) attempts to automate this process by means of fusion o...

When the covariance matrix formulation is used for multilook polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution can be used for change detection between acquisitions at two or more time points. Here, we are concerned with the analysis of change between two time points and the "direction" of change: Does the radar res...

Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution and a factorization of this test statistic with associated p-values, change analysis in a time series of multilook polarimetric SAR data in the covariance matrix representation is carried out. The o...

When the covariance matrix formulation is used for multi-look polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution a test statistic for equality of two complex variance-covariance matrices and an associated asymptotic probability of obtaining a smaller value of the test statistic are...

Data-driven classification algorithms have proved to do well for automatic target recognition (ATR) in synthetic aperture radar (SAR) data. Collecting data sets suitable for these algorithms is a challenge in itself as it is difficult and expensive. Due to the lack of labeled data sets with real SAR images of sufficient size, simulated data play a...

This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA). Where CCA is based on maximising correlation betwe...

Convolutional Neural Networks (Convnets) have achieved good results in a range of computer vision tasks the recent years. Though given a lot of attention, visualizing the learned representations to interpret Convnets, still remains a challenging task. The high dimensionality of internal representations and the high abstractions of deep layers are t...

Automated monitoring systems that can capture wetlands' high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentin...

Reconstruction of historical Arctic sea level is generally difficult due to the limited coverage and quality of both tide gauge and altimetry data in the area. Here a strategy to achieve a stable and plausible reconstruction of Arctic sea level from 1950 to today is presented. This work is based on the combination of tide gauge records and a new 20...

Monitoring of long-term land-use and land-cover change patterns may be biased by seasonal changes of different surface properties (e.g. hydrology, phenology, etc.) which become even more prominent in highly dynamic ecosystems such as wetlands (Crews-Meyer, 2008; McClearly, Crews-Meyer and Young 2008; Dronova et al. 2011). These surface dynamics pro...

We present the likelihood ratio test statistic for the homogeneity of several complex variance–covariance matrices that may be used in order to assess whether at least one change has taken place in a time series of SAR data. Furthermore, we give a factorization of this test statistic into a product of test statistics that each tests simpler hypothe...

When the covariance matrix representation is used for multi-look polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution a likelihood ratio test statistic for equality of two complex variance-covariance matrices and an associated p-value are given. In a case study airborne EMISAR C- and...

When the covariance matrix formulation is used for multilook polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution, a test statistic for equality of two complex variance–covariance matrices and an associated asymptotic probability of obtaining a smaller value of the test statistic are...

The paper describes the development and testing of a simulation tool, called QualiSIM. The tool estimates GNSS-based position accuracy based on a simulation of the environment surrounding the GNSS antenna, with a special focus on city-scape environments with large amounts of signal reflections from non-line-of-sight satellites. The signal reflectio...

In this article, a novel after-disaster building damage monitoring method is presented. This method combines the multispectral imagery and digital surface models (DSMs) from stereo matching of two dates to obtain three kinds of changes: collapsed buildings, newly built buildings and temporary shelters. The proposed method contains three basic steps...

Canonical correlation analysis is an established multivariate statistical method in which correlation between linear combinations of multivariate sets of variables is maximized. In canonical information analysis introduced here, linear correlation as a measure of association between variables is replaced by the information theoretical, entropy base...

In this paper, we seek an appropriate selection of tide gauges for Arctic Ocean sea-level reconstruction based on a combination of empirical criteria and statistical properties (leverages). Tide gauges provide the only in situ observations of sea level prior to the altimetry era. However, tide gauges are sparse, of questionable quality, and occasio...

The goal of this paper is to develop an efficient method for forest change detection using multitemporal stereo panchromatic imagery. Due to the lack of spectral information, it is difficult to extract reliable features for forest change monitoring. Moreover, the forest changes often occur together with other unrelated phenomena, e.g., seasonal cha...

A test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution is introduced. The test statistic is applied successfully to detect change in C-band EMISAR polarimetric SAR data over four time points.

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformationof the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) canbe used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signalsources composing a given hyperspectral...

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformationof the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) canbe used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signalsources composing a given hyperspectral...

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.

The Danish national elevation model, DK-DEM, was introduced in 2009 and is based on LiDAR data collected in the time frame 2005-2007. Hence, DK-DEM is aging, and it is time to consider how to integrate new data with the current model in a way that improves the representation of new landscape features, while still preserving the overall (very high)...

In this paper, a novel disaster building damage monitoring method is presented. This method combines the multispectral imagery and DSMs from stereo matching to obtain three kinds of changes. The proposed method contains three basic steps. The first step is to segment the panchromatic images to get the smallest possible homogeneous regions. In the s...

Canonical correlation analysis (CCA) maximizes the correlation between two sets of multivariate data. CCA is applied to multivariate satellite data and univariate radar data to produce a subspace descriptive of heavily precipitating clouds. A misalignment, inherent to the nature of the two datasets, was observed, corrupting the subspace. A method f...

We examine the scale and spatial distribution of the mass change acceleration in Greenland and its statistical significance, using processed gravimetric data from the GRACE mission for the period 2002–2011. Three different data products – the CNES/GRGS, DMT-1b and GGFC GRACE solutions – have been used, all revealing an accelerating mass loss in Gre...

This contribution deals with classification of multilook fully polarimetric synthetic aperture radar (SAR) data by learning a dictionary of crop types present in the Foulum test site. The Foulum test site contains a large number of agricultural fields, as well as lakes, wooded areas, natural vegetation, grasslands and urban areas, which makes it id...

Currently, no objective method exists for estimating the rate of change in the colour of meat. Consequently, the purpose of this work is to develop a procedure capable of monitoring the change in colour of meat over time, environment and ingredients. This provides a useful tool to determine which storage environments and ingredients a manufacturer...

Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF analysis. We consider the model signal-to-noise ratio (S...

The mass loss of the Greenland Ice Sheet (GrIS) has previously been analysed in a variety of ways, including altimetry, gravimetry and mass budget calculations, establishing a continuing decrease in the ice mass, with a number of studies finding acceleration in the mass loss. Here, we examine this acceleration and its statistical significance, usin...

Ocean satellite altimetry has provided global sets of sea level data for the last two decades, allowing determination of spatial patterns in global sea level. For reconstructions going back further than this period, tide gauge data can be used as a proxy. We examine different methods of combining satellite altimetry and tide gauge data using optima...

The iteratively reweighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsupervised change detection in multi- and hyperspectral remote sensing imagery and for automatic radiometric normalization of multitemporal image sequences. Principal components analysis (PCA), as well as maximum autocorrelation factor (MAF) and m...

In this article, a new assessment system is presented to evaluate infrastructure objects such as roads after natural disasters in near-realtime. A particular aim is the exploitation of multi-sensor and multi-temporal imagery together with further geographic information system data in a comprehensive assessment framework. The combination is accompli...

This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very similar although the loadings are very different from...

This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is ex...

This paper gives an introductory analysis of gravity data from the GRACE (Gravity Recovery And Climate Experiment) twin satellites. The data consist of gravity data in the form of 10-day maximum values of 1° by 1° equivalent water height (EWH) in meters starting at 29 July 2002 and ending at 25 August 2010. Results focussing on Greenland show stati...

This paper introduces kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analysis. The kernel versions are based upon a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version, the inner products of the ori...

Based on canonical correlation analysis the iteratively re-weighted multivariate alteration detection (MAD) method is used to successfully perform unsupervised change detection in bi-temporal Landsat ETM+ images covering an area with villages, woods, agricultural fields and open pit mines in North Rhine-Westphalia, Germany. A link to an example wit...

The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper-vised change detection in multi-and hyperspectral remote sensing imagery as well as for automatic radiometric normalization of multi-or hypervariate multitemporal image sequences. Principal component analysis (PCA) as well as maximum autoco...

Principal component analysis (PCA) is often used for general feature generation and linear orthogonalization or compression by dimensionality reduction of correlated multivariate data, see Jolliffe for a comprehensive description of PCA and related techniques. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent refer...

Based on orthorectified, bi-temporal 2,000x2,000 5 m pixel multispectral RapidEye data [1] short-term changes are detected associated with land-use and reclamation in connection with open pit mining in North Rhine-Westphalia, Germany. The changes are found automatically by means of a combination of the iteratively re-weighted MAD method [2], which...

Change over time between two 512 by 512 (25 m by 25 m pixels) multispectral Landsat Thematic Mapper images dated 6 June 1986 and 27 June 1988 respectively covering a forested region in northern Sweden, is here detected by means of the iteratively reweighted multivariate alteration detection (IR-MAD) method followed by post-processing by means of ke...

A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly, and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the k...

Kernel versions of the principal components (PCA) and maximum autocorrelation factor (MAF) trans-formations are used to postprocess change images obtained with the iteratively re-weighted multivari-ate alteration detection (MAD) algorithm. It is found that substantial improvements in the ratio of signal (change) to background noise (no change) can...

Many change detection algorithms work by calculating the probability of change on a pixel-wise basis. This is a disadvantage since one is usually looking for regions of change, and such information is not used in pixel-wise classification - per definition. This issue becomes apparent in the face of noise, implying that the pixel-wise classifier is...

In this paper we present an exploratory analysis of hyper-spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumination. In order to explore the hyperspectral data we compare a series of subspace projection methods including principal component analysis and maximum autoc...

Principal component analysis (PCA) is the mother of all linear orthogonal transformations for data compression and dimensionality reduc-tion of correlated multivariate data. This contribution describes a kernel version of PCA and it also sketches kernel versions of maximum autocorrelation factor (MAF) analysis and minimum noise fraction (MNF) analy...

This paper introduces several unique image processing and interpretation techniques that can be used to monitor and verify arms control treaties. It is argued in the paper that not only has there been great improvement in the spatial and temporal resolution of commercial satellite imagery providing the international community with the means to moni...

Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical region acquired at two different time points. If ch...

A method is proposed for pixel-level satellite image fusion derived directly from a model of the imaging sensor. By design, the proposed method is spectrally consistent. It is argued that the proposed method needs regularization, as is the case for any method for this problem. A framework for pixel neighborhood regularization is presented. This fra...

Le projet Hjortekar de six types de maison consommant peu d'énergie au nord de Copenhague est devenu célèbre. Dans cet article, les auteurs du Laboratoire Isolation Thermique de l'Université Technique du Danemark exposent certains détails de construction permettant d'éviter les ponts thermiques, dont un élément porteur d'un nouveau type, et d'assur...

A recently proposed method for automatic radiometric normalization of multi- and hyperspectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no-change probabilities. The procedure is first investi...

The iteratively re-weighted multivariate alteration detection (IR-MAD) transformation is proving to be very successful for multispectral change detection and automatic radiometric normalization applications in remote sensing. Various alternatives exist in the way in which the weights (no-change probabilities) are calculated during the iteration pro...

Multi-look, polarimetric synthetic aperture radar (SAR) data are often worked with in the so-called covariance matrix representation. For each pixel this representation gives a 3 times 3 Hermitian, positive definite matrix which follows a complex Wishart distribution. Based on this distribution a test statistic for equality of two such matrices and...

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted (IR) MAD method in a series of iterations places inc...

The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods are demonstrated with an example involving bitemporal...

Change detection is a very important application of Earth observation data. A number of different applications relies on robust and accurate change detection from such data. Update of topographic maps is, for instance, a very important process for mapping agencies to be able to provide the most up-to-date map information to users. The update of top...

A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect information on precipitation in the atmosphere from Meteosa...

We analyze multispectral reflectance images of concrete aggregate material, and design computational measures of the important and critical parameters used in concrete production. The features extracted from the images are exploited as explanatory variables in regression models and used to predict aggregate type, water content, and size distributio...

Change detection methods for multi- and hyper-variate data aim at identifying differences in data acquired over the same area at different points in time. In this contribution an iterative extension to the multivariate alteration detection (MAD) transformation for change detection is sketched and applied. The MAD transformation is based on canonica...

We introduce an interactive segmentation method for a sea floor survey. The method is based on a deformable template classifier and is developed to segment data from an echo sounder post-processor called RoxAnn. RoxAnn collects two different measures for each observation point, and in this 2D feature space the ship-master will be able to interactiv...

Change detection methods for multi- and hypervariate data aim at identifying differences in data acquired over the same area at different points in time. In this contribution a regularized, iterative extension to the multivariate alteration detection (MAD) transformation for change detection is sketched and applied. The MAD transformation is based...

The application of a sensitive change detection procedur e for nuclear treaty verification is examined in case studies involving under ground nuclear testing and location of clandestine uranium mining activities.

The statistical techniques of multivariate alteration detection, maximum autocorrelation factor transformation, expectation maximization, fuzzy maximum likelihood estimation and probabilistic label relaxation are combined in a unified scheme to classify changes in multispectral satellite data. An example involving bitemporal LANDSAT TM imagery is g...

In order to facilitate the update of the building theme in photogrammetrically derived GIS databases, we investigate spectra, textural, and shape features of areas of aerial photos previously registered as buildings. In following steps, these features are used in a classification tree characterisation of the entire photo, and a simple classificatio...

The linear scale invariance of the multivariate alteration detection (MAD) transformation is used to obtain invariant pixels for automatic relative radiometric normalization of time series of multispectral data. Normalization by means of ordinary least squares regression method is compared with normalization using orthogonal regression. The procedu...

Nowadays, the medical tracking of dermatological diseases is imprecise, mainly due to the lack of suitable objective methods to evaluate the lesion. The severity of the disease is currently scored by doctors merely by means of visual examination. In this work, multiset canonical correlation analysis over registered images is proposed to track the e...

The multi-variate alteration detection transform is applied to pairs of within and between time varying registered psoriasis image patterns. Color band contribution to the variates explaining maximal change is analyzed.