496 research outputs found
Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data For Improvement of Land Cover Classification
No full textThe interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. This is because a combined use is supposed to improve the results of classification tasks compared to single-data use. This paper addressed using of combination of hyperspectral and Light Detection And Ranging (LIDAR) data in classification field.
This paper presents a new method based on the definition of a Multiple Classifier System on Hyperspectral and LIDAR data. In the first step, the proposed method applied some feature extraction strategies on LIDAR data to produce more information in this data set. After that in second step, Support Vector Machine (SVM) applied as a supervised classification strategy on LIDAR data and hyperspectal data separately. In third and final step of proposed method, a classifier fusion method used to fuse the classification results on hypersepctral and LIDAR data. For comparative purposes, results of classifier fusion compared to the results of single SVM classifiers on Hyperspectral and LIDAR data. Finally, the results obtained by the proposed classifier fusion system approach leads to higher classification accuracies compared to the single classifiers on hyperspectral and LIDAR data
Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection
No full textThe generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct
correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed
Urban object detection using a fusion approach of dense urban digital surface models and VHR optical satellite stereo data
No full textIn this paper we describe a new approach for the extraction of urban objects from very high resolution (VHR) optical stereo satellite imagery. Such data is delivered from sensors like Ikonos, QuickBird, GeoEye or WorldView-II. These sensors provide ground sampling distances (GSD) of 0.5 to 1 m for the pan chromatic channel and 2 to 4 m for the multispectral channels. Normally good digital surface models (DSM) can only be expected at 1/3 to 1/5 of the original GSD. But we present a new approach which uses the generation of dense disparity maps based on computer vision approches and fuse these disparity maps with additional information gained from
the original imagery to allow the extraction and afterwards modeling of urban objects. This can be achieved due to the fact that the generated disparity maps are constructed on one of the original images. So a direct pixel to pixel correlation of the height (represented by the disparity) and the spectral information (represented by the pan sharpened original image) can be done. Applying methods for the generation of a digital terrain model (DTM) which represents the ground without elevated objects and spectral classification allows the separation of typical urban classes like buildings, trees, roads, low vegetation, water and so on. These classes will be treated individually in the modeling step to generate a simplified 3D model of the observed urban area. The results are presented, compared to the original imagery and discussed
Multi-sensor remote sensing information fusion for urban area classification and change detection
No full textInformation extraction from multi-sensor remote sensing imagery is an important and challenging task for many applications such as urban area mapping and change detection. A special acquisition (orthogonal) geometry is of great importance for optical and radar data fusion. This acquisition geometry allows to minimize displacement effects due inaccuracy of Digital Elevation Model (DEM) used for data ortho-rectification and existence of unknown 3D structures in a scene. Final data spatial alignment is performed by recently proposed co-registration method based on a Mutual Information measure. For a combination of features originating from different sources, which are quite often non-commensurable, we propose an information fusion framework called INFOFUSE consisting of three main processing steps: feature fission (feature extraction aiming at complete description of a scene), unsupervised clustering (complexity reduction and feature representation in a common dictionary) and supervised classification realized by Bayesian or Neural networks. An example of urban area classification is presented for the orthogonal acquisition of space borne very high resolution WorldView-2 and TerraSAR-X Spotlight imagery over Munich city, South Germany. Experimental results confirm our approach and show a great potential also for other applications such as change detection
Fusion of multi-spectral bands and DSM from Worldview-2 Stereo imagery for building extraction
No full textMulti-resolution, multi-sensor image fusion: general fusion framework
No full textMulti-resolution image fusion also known as pansharpening
aims to include spatial information from a high
resolution image, e.g. panchromatic or Synthetic Aperture Radar
(SAR) image, into a low resolution image, e.g. multi-spectral or
hyper-spectral image, while preserving spectral properties of a
low resolution image. A signal processing view at this problem
allowed us to perform a systematic classification of most known
multi-resolution image fusion approaches and resulted in a
General Framework for image Fusion (GFF) which is very well
suitable for a fusion of multi-sensor data such as optical-optical
and optical-radar imagery. Examples are presented for
WorldView-1/2 and TerraSAR-X data
Information extraction using optical and radar remote sensing data fusion
No full textInformation extraction from multi-sensor remote sensing imagery is an important and challenging task for many
applications such as urban area mapping and change detection. Especially for optical and radar data fusion a special
acquisition (orthogonal) geometry is of great importance in order to minimize displacements due to an inaccuracy of
the Digital Elevation Model (DEM) used for data ortho-rectification and due to the presence of unknown 3D
structures in a scene. Final data spatial alignment is performed manually using ground control points (GCPs) or by a
recently proposed automatic co-registration method based on a Mutual Information measure. These data preprocessing
steps are of a crucial importance for a success of the following data fusion. For a combination of features
originating from different sources, which are quite often non-commensurable, we propose an information fusion
framework called INFOFUSE consisting of three main processing steps: feature fission (feature extraction for
complete description of a scene), unsupervised clustering (complexity reduction and feature conversion to a
common domain) and supervised classification realized by Bayesian/Neural/Graphical networks. Finally, a general
data processing chain for multi-sensor data fusion is presented. Examples of buildings in an urban area are presented
for very high resolution space borne optical WorldView-2 and radar TerraSAR-X imagery over Munich city,
Germany in different acquisition geometries including the orthogonal one. Additionally, theoretical analysis of radar
signatures of buildings in urban area and its impact on the joint classification or data fusion is discussed
Traffic congestion parameter estimation in time series of airborne optical remote sensing images
No full textIn this paper we propose a new model based traffic parameter estimation approach in congested situations in time series of airborne optical remote sensing data. The proposed approach is based on the combination of various techniques: change detection, image processing and incorporation of a priori information such as road network, information about vehicles and roads and finally a traffic model. The change detection in two images with a short time lag of several seconds is implemented using the multivariate alteration detection method resulting in a change image where the moving vehicles on the roads are highlighted. Further, image processing techniques are applied to derive the vehicle density in the binarized change image. Finally, this estimated vehicle density is related to the vehicle density, acquired by modelling the traffic flow for a road segment. The model is derived from a priori information about the vehicle sizes and road parameters, the road network and the spacing between the vehicles. Then, the modelled vehicle density is directly related to the average vehicle velocity on the road segment and thus the information about the traffic situation can be derived. To confirm our idea and to validate the method several flight campaigns with the DLR airborne experimental wide angle optical 3K digital camera system operated on a Do-228 aircraft were performed. Experiments are performed to analyse the performance of the proposed traffic parameter estimation method for highways and main streets in the cities. The estimated velocity profiles coincide qualitatively and quantitatively quite well with the reference measurements
On the Possibility of Intensity Based Registration for Metric Resolution SAR and Optical Imagery
No full textMultimodal image registration is a key to many remote sensing tasks like fusion, change detection, GIS overlay operations, 3D visualization etc. With advancements in research, intensity based similarity metrics namely mutual information (MI) and cluster reward algorithm (CRA) have been utilized for intricate multimodal registration problem. The computation of these metrics involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g. SAR and optical). Modern day satellites like TerraSAR-X and IKONOS provide high resolution images generating enormous data volume along with very different image radiometric properties (especially in urban areas) not observed ever before. Thus, performance evaluation of intensity based registration techniques for metric resolution imagery becomes an interesting case study. In this paper, we analyze the performance of similarity metrics namely, mutual information and cluster reward algorithm for metric resolution images acquired over both plain and urban/semi-urban areas. Techniques for handling the generated enormous data volume and influence of really different sensor geometries over images especially acquired over urban areas have also been proposed and rightfully analyzed. Our findings from three carefully selected datasets indicate that the intensity based techniques can still be utilized for high resolution imagery but certain adaptations (like compression and segmentation) become useful for meaningful registration results
Classification accuracy increase using multisensor data fusion
No full textThe practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.)
but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the
confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification
products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral
data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since
this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed
for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution
SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and
multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised
clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network).
This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced
by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion
of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types
of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results
of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other
established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport
objects, forest, roads, rail roads, etc
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