Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data.
Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades.
Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become
popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to
characterize the background. The global SVDD needs a training set for the background modeling; however, it is sensitive
to outliers in the data; so the training set has to be generated with pure background spectra. In general, the training data is
selected by random selection of the pixels spectra in entire image. In this study, we propose an approach for better
selection of the training data based on principal component analysis (PCA). A valid assumption for remotely sensed
images is that the principal components (PCs) with higher variance include substantial amount of background
information. For this reason, a subspace composed of several of the highest variance PCs of cluttered data can be defined
as background subspace. Thus, with the proposed algorithm, the selection of background pixels is achieved by projecting
all pixels in the image into the background subspace and thresholding them with respect to the relative energy on the
background subspace. Experimental results verify that the proposed algorithm has promising results in terms of accuracy
and speed during the detection of anomalies.
In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images
for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic
Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant
of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling
(FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction
and shape detail preservation. MSERs of each scale space image are found and then combined through a
decision level fusion strategy, namely “selective scale fusion” (SSF), where contrast and boundary curvature of
each MSER are considered. The performance of the proposed method is evaluated using real multitemporal
high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with
several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change.
One of the main outcomes of this approach is that different objects having different sizes and levels of contrast
with their surroundings appear as stable regions at different scale space images thus the fusion of results from
scale space images yields a good overall performance.
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.
Automatic target detection (ATD) methods for synthetic aperture radar (SAR) imagery are sensitive to image resolution,
target size, clutter complexity, and speckle noise level. However, a robust ATD method needs to be less sensitive to the
above factors. In this study, a constant false alarm rate (CFAR) based method is proposed which can perform target
detection independent of image resolution and target size even in heterogeneous background clutter. The proposed
method is computationally efficient since clutter statistics are calculated only for candidate target regions and a single
execution of the method is sufficient for different types of targets having different shapes and sizes. Computational
efficiency is further increased by parallelizing the algorithm using OpenMP and NVidia CUDA implementations.
In this paper, we propose a blind watermarking method where watermark is chosen as the hologram of the signal to be
embedded. In the proposed approach the quantized phase of the hologram is embedded into an image using quantization
index modulation (QIM). The robustness of the proposed technique is tested against several attacks such as filtering,
compression, occlusion and cropping. Also the effects of quantization of the hologram on the reconstruction quality are