Rik Bellens

Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.

Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based MP gives a substantial improvement of the classification result. A second improvement is achieved by using ldquopartial morphological reconstructionrdquo instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to ldquoover-reconstructionrdquo in typical remote sensing images and a decreased classification performance. With ldquopartial reconstruction,rdquo we are able to overcome this problem and still preserve the shape of objects.

Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction

When using morphological features for the classification of high resolution hyperspectral images from urban areas, one should consider two important issues. The first one is that classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction. The second one is that the morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. In this paper, we first investigate morphological profiles with partial reconstruction and directional MPs for the classification of high resolution hyperspectral images from urban areas. Secondly, we develop a semi-supervised feature extraction to reduce the dimensionality of the generated morphological profiles for the classification. Experimental results on real urban hyperspectral images demonstrate the efficiency of the considered techniques.

Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.

Morphological Attribute Profiles With Partial Reconstruction

Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.

Generalized pixel profiling and comparative segmentation with application to arteriovenous malformation segmentation.

Extraction of structural and geometric information from 3-D images of blood vessels is a well known and widely addressed segmentation problem. The segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, with a special application in diagnostics and surgery on arteriovenous malformations (AVM). However, the techniques addressing the problem of the AVM inner structure segmentation are rare. In this work we present a novel method of pixel profiling with the application to segmentation of the 3-D angiography AVM images. Our algorithm stands out in situations with low resolution images and high variability of pixel intensity. Another advantage of our method is that the parameters are set automatically, which yields little manual user intervention. The results on phantoms and real data demonstrate its effectiveness and potentials for fine delineation of AVM structure.

The use of smartphone applications in the collection of travel behaviour data

The MOVE project deals with the collection and analysis of crowd behaviour data. The main goals of the project are to collect data through the use of mobile phones and to develop new technologies to process and mine the collected data for crowd behaviour analysis. This paper describes the different steps in the development of tracking applications for smartphones that make use of advanced data mining. The results on data collection, analysis, and reporting have led to the development and operation of an advanced urban data monitoring system.

Classification of hyperspectral data over urban areas based on extended morphological profile with partial reconstruction

Extended morphological profiles with reconstruction are widely used in the classification of very high resolution hyperspectral data from urban areas. However, morphological profiles constructed by morphological openings and closings with reconstruction can lead to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction. In this paper, we apply extended morphological profiles with partial reconstruction (EMPP) to the classification of high resolution hyperspectral images from urban areas. We first used feature extraction to reduce the dimensionality of the hyperspectral data, as well as reduce the redundancy within the bands, then constructed EMPP on features extracted by PCA, independent component analysis and kernel PCA for the classification of high resolution hyperspectral images from urban areas. Experimental results on real urban hyperspectral image demonstrate that the proposed EMPP built on kernel principal components gets the best results, particularly in the case with small training sample sizes.

Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyper-spectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.

Relevance Criteria for Spatial Information Retrieval Using Error-Tolerant Graph Matching

In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential