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We address the problem of reducing the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps...
We propose a method to effectively extract lattice openings (windows and doors) of traditional Japanese buildings from terrestrial light detection and ranging data. First, the points on a wall are extracted and edges that indicate non-flat surfaces are selected by examining shifts that occur when the data are smoothed. Next, wall direction is determined. Following this, the edges are grouped along...
This paper presents a novel model of spectrum shape elements for correction of non-isoplanatic deviation in illumination. The model is applicable for color and hyperspectral images. We propose a technique for identification of the correction transform on the set of spectrum shape elements with the Hausdorff metric. In addition, a necessary condition, which allows obtaining an adequate form of the...
This paper presents a preliminary approach for debris detection in SAR images based on simulated training are-as. For this purpose radiometrically correct simulations of heaps of debris are produced. Based on statistics of the first and second order they are analyzed for their textural characteristics. The resulting feature information is used for the localization of de-bris-like signatures in real...
In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed...
Due to the high dimensionality of hyperspectral data, dimension reduction is becoming an important problem in hyperspectral image classification. Band selection can retain the information which is capable of keeping the original meaning of the data, and thus has attracted more attention. This paper tackles the band selection problem from the perspective of multiple classifiers combination, which can...
We propose a new method for spectral feature extraction based on Orthogonal Polynomial Function (OPF) fitting. Given a spectral signature, it is firstly divided into spectral segments by a splitting strategy. All segments are fitted by using OPF respectively. The features of input spectrum are selected from the fitting coefficients of all segments. 10 laboratory spectra of various materials are selected...
This paper proposes a method for estimating the green space ratio in urban areas by using airborne LiDAR and aerial photographs. The index is defined as the ratio of an area occluded by vegetation to the whole of an area in an azimuth-elevation angle space. Vegetation is detected by a combination of segmented LiDAR data point clouds and image brightness data. The occlusion by vegetation is calculated...
In this paper, we introduce an efficient and automatic method for road extraction from satellite or aerial images. It builds upon an existing work based on (incomplete) path opening/closing, morphological filters able to deal with curvilinear structures. We propose here to apply such techniques not on pixels directly but rather on regions representing road segments, to improve both efficiency and...
A new hyper-spectral data set is at hand giving unique possibilities for investigating also multi-scale evidence fusion. In this contribution self-organizing maps are used for semi-supervised learning and visualization of the partially labeled data. The maps reveal that the seven classes given can be better distinguished using certain color and rotationally invariant texture features on the high-resolution...
We propose a method to delineate buildings from aerial images and rapidly count the number of buildings damaged by a disaster. Traditional segmentation techniques may divide a single building into multiple structures when some parts of the building are in shadow and others are not. The proposed method first segments regions from aerial images by using a method robust to shadows. Then, point clouds...
As the golden standard in robust estimation, the classic RANSAC approach has undergone extensive research that contributed to further enhancements in run-time performance, robustness, and multi-structure support to name a few. Yet, the accelerating growth of multi-modal co-registered datasets requires a new adaptation of the RANSAC algorithm. In this paper, we propose a multi-modal fault-tolerant...
This paper discusses the potentials and challenges of the recognition of individual tree patterns in image data acquired by decimeter-resolution airborne synthetic aperture radar (SAR) systems working in the millimeterwave domain. Due to the different characteristics of conventional optical imagery and side-looking SAR imagery, sophisticated processing strategies have to be developed, which need to...
This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-specific spatial-spectral dictionary, which is used for a sparse coding procedure for the reconstruction and classification...
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