Pre-processing, thresholding and post-processing stages are very important especially for very small target detection from infrared images. The effects of these stages to the final detection performance are measured in this study. Various methods for each stage are compared based on the final detection performance, which is defined by precision and recall values. Among various methods, the best method for each stage is selected and proved. For the pre-processing stage, local block based methods perform the best, nearly for all thresholding methods. The best thresholding method is chosen as the one, which does not need any user defined parameter. Finally, the post processing method, which is suitable for the best performing pre-processing and thesholding methods is selected.
Top-Hat transform is well known background suppression method used in small target detection. In this paper, we investigate various different Top-Hat transformation based small target detection approaches. All of the methods are implemented with their best parameter settings and applied to the same test image. The comparison among them is done in terms of three issues: 1. the detection performance (precision and false alarm rate), 2. the time requirement of the method and its usability for real time applications, 3. the number of parameters, which need user interaction. Results show that all of the algorithms require a prior knowledge of target size, which is either used as the structuring element size or as the threshold for post-processing. Algorithms, which use automatic approaches to select its parameters, are not generic to be applied to various images. But algorithms, which use adaptive methods for deciding on the threshold value, perform better than the others.
A fully automatized method which can extract road networks by using the spectral and structural features of the roads is
proposed. First, Anti-parallel Centerline Extraction (ACE) is used to obtain road seed points. Then, the road seeds are
improved with perceptual grouping method and the road class is determined with Maximum Likelihood Estimation
(MLE) by modeling the seed points with Gaussian Mixture. The morphological operations (opening, closing and
thinning) are performed for improving classification results and determining the road topology roughly. Finally,
perceptual grouping is performed for removing non-road line segments and filling the gaps on the topology. The
proposed algorithm is tested on 1 meter resolution IKONOS images and results better than previous algorithms are
obtained.
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.
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.
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