This study proposes a new method to detect road junctions from existing road masks obtained from geospatial databases. Moreover, this method can be used to extract junction points from the road masks generated by automatic or semiautomatic road extraction algorithms. The algorithm is intended to lower the false detection rate by refining the input road mask. Vector space analysis of the pruned road skeleton provides a simple yet robust detection and classification strategy. Empirical results demonstrate the success of the proposed junction extraction model.
In this study, a supervised method for automatic road detection based on spectral indices and structural properties is proposed. The need of generalizing the spectral features for the images captured by different kinds of devices is investigated. Mean-shift segmentation algorithm is employed to partition the input multi-spectral image in addition to k-means which is used as a complementary method for structural feature generation. Adaboost learning algorithm is utilized with extracted features to distinguish roads from non-road regions in the satellite images. The proposed algorithm is tested on an image database containing both IKONOS and GEOEYE images to verify the achieved generalization. The empirical results show that the proposed road extraction method is promising and capable of finding the majority of the road network.
Data-driven unsupervised segmentation of high resolution remotely sensed images is a primary step in understanding
remotely sensed images. A new fully automatic method to delineate the segments corresponding to objects in high
resolution remotely sensed images is introduced. There are extensive methods proposed in the literature which are
mainly concentrated on pixel level information. The proposed method combines the structural information extracted by
morphological processing with feature space analysis based on mean shift algorithm. The spectral and spatial bandwidth
parameters of mean shift are adaptively determined by exploiting differential morphological profile (DMP). Spectral
bandwidth is determined in relation to the first maximum value of DMP at each pixel and spatial bandwidth is
determined by the corresponding index in DMP. In this method there is also no need to specify initially the maximum
size of the structuring element for the morphological processes. By the use of mean shift filtering, the feature space
points are grouped together which are close to each other both in the range of spatial and spectral bandwidths. The
proposed method is applied on panchromatic high resolution QuickBird satellite images taken from urban areas. The
results we obtained appear to be effective in terms of segmentation and combining the spectral and spatial information to
extract more precise and more meaningful objects compared to fixed bandwidth mean shift segmentation.
Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing
satellites, have urged the development of automatic target detection systems in satellite images. Automatic detection of
airports is particularly essential, due to the strategic importance of these targets. In this paper, a runway detection method
using a segmentation process based on textural properties is proposed for the detection of airport runways, which is the
most distinguishing element of an airport. Several local textural features are extracted including not only low level
features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin
Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis.
Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other
structures and landforms, cannot be predicted trivially, Adaboost learning algorithm is employed for both classification
and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a
coarse representation of possible runway locations is obtained. Promising experimental results are achieved and given.
Present study concerns the problem of learning, pattern recognition and computational abilities of a homogeneous network composed from coupled bistable units. An efficient learning algorithm is developed. New possibilities for pattern recognition may be realized due to the developed technique that permits a reconstruction of a dynamical system using the distributions of its attractors. In both cases the updating procedure for the coupling matrix uses the minimization of least-mean-square errors between the applied and desired patterns.