Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

Interactive ship segmentation in SAR images (SAR görüntülerinde etkileşimli gemi bölütleme)

Ship detection from synthetic aperture radar (SAR) images is important for various automatic target recognition (ATR) tasks. Although the ships in offshore areas can be easily detected, the ones near the shores or close to each other are difficult to detect. Furthermore, segmentation and classification of such ships is extremely difficult. In this study, a novel approach is presented for the fast and accurate segmentation of ship boundaries with minimal user interaction. In this approach, the rough location and orientation of a ship is determined by the user. Then, a ship model, which is constructed from synthetic ship images, is fitted on to the ship selected by the user and accurate ship boundaries are extracted. The effectiveness of the proposed algorithm is demonstrated by experimental results

Information-theoretic noisy band detection in hyperspectral imagery (Hiperspektral görüntülerde gürültülü bantların bilişim kuramsal tespiti)

Hyperspectral imagery consists of hundreds of successive bands that carry spectral information about the underlying materials at various wavelengths. However, due to practical factors such as atmospheric effects and sensor characteristics, some spectral bands contain high amounts of noise. In this paper, an effective information-theoretic algorithm based on mutual information that automatically detects such noisy bands is proposed. The effectiveness and accuracy of the proposed approach is validated on hyperspectral images collected by the AVIRIS and TELOPS sensors. Experimental results show that the proposed method outperforms the other algorithms in the literature

SAR süperpikseller üretimi için benzerlik oran tabanlı algoritmalar.

Synthetic Aperture Radar (SAR) has the capability of working in all weather conditions during day and night that makes it attractive to be used for automatic target detection and recognition purposes. However, it has the problem of high amount of multiplicative speckle noise. Superpixel segmentation as a preprocessing step is an oversegmentation technique that groups similar neighboring pixels into regularly organized segments with approximately the same size. As boundaries of the objects are important elements to be traced, superpixels should adhere well to the edges. This can only be achieved by an algorithm robust to speckle noise. In this thesis, similarity ratio is first developed as a new metric that is robust to speckle noise. Secondly, Mahalanobis distance is used instead of Euclidian so that the superpixel can fit better to shapes in the real world. Thirdly, the constant determining the relative importance of radiometric and geometric terms is replaced with an adaptive function. The performance of combinations of similarity ratio with Euclidean distance (SREP), Mahalanobis distance (SRMP) and Mahalanobis distance with adaptive scheme (SRAMP) are evaluated by conducting experiments on real and synthetic images. The experimental results showed that similarity ratio and adaptive Mahalanobis proximity (SRAMP) outperforms the other approaches in terms of uniformity, compactness and visual appearance.Ph.D. - Doctoral Progra