Land-cover classification in Synthetic Aperture Radar (SAR) images has significance in both civil and military remote sensing applications. Accurate classification is a challenging problem due to variety of natural and man-made objects, seasonal changes at acquisition time, and diversity of image reconstruction algorithms.. In this study, Feature Preserving Despeckling (FPD), which is an edge preserving total variation based speckle reduction method, is applied as a preprocessing step. To handle the mentioned challenges, a novel feature extraction schema combined with a super-pixel segmentation and dictionary learning based classification is proposed. Computational complexity is another issue to handle in processing of high dimensional SAR images. Computational complexity of the proposed method is linearly proportional to the size of the image since it does not require a sliding window that accesses the pixels multiple times. Accuracy of the proposed method is validated on the dataset composed of TerraSAR-X high resolutions spot mode SAR images.
Speckle noise which is inherent to Synthetic Aperture Radar (SAR) imaging obstructs various image exploitation tasks such as edge detection, segmentation, change detection, and target recognition. Therefore, speckle reduction is generally used as a first step which has to smooth out homogeneous regions while preserving edges and point scatterers. Traditional speckle reduction methods are fast and their memory consumption is insignificant. However, they are either good at smoothing homogeneous regions or preserving edges and point scatterers. State of the art despeckling methods are proposed to overcome this trade-off. However, they introduce another trade-off between denoising quality and resource consumption, thereby higher denoising quality requires higher computational load and/or memory consumption. In this paper, a local pixel-based total variation (TV) approach is proposed, which combines l<sub>2</sub>-norm and l<sub>1</sub>-norm in order to improve despeckling quality while keeping execution times reasonably short. Pixel-based approach allows efficient computation model with relatively low memory consumption. Their parallel implementations are also more efficient comparing to global TV approaches which generally require numerical solution of sparse linear systems. However, pixel-based approaches are trapped to local minima frequently hence despeckling quality is worse comparing to global TV approaches. Proposed method, namely mixed norm despeckling (MND), combines l<sub>2</sub>-norm and l<sub>1</sub>-norm in order to improve despeckling performance by alleviating local minima problem. All steps of the MND are parallelized using OpenMP on CPU and CUDA on GPU. Speckle reduction performance, execution time and memory consumption of the proposed method are shown using synthetic images and TerraSAR-X spot mode SAR images.
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.
Cortical renal (kidney) scintigraphy images are 2D images (256x256) acquired in three projection angles (posterior,
right-posterior-oblique and left-posterior-oblique). These images are used by nuclear medicine specialists to examine
the functional morphology of kidney parenchyma. The main visual features examined in reading the images are: size,
location, shape and activity distribution (pixel intensity distribution within the boundary of each kidney). Among the
above features, activity distribution (in finding scars if any) was found to have the least interobserver reproducibility.
Therefore, in this study, we developed an image-based retrieval (IBR) and a computer-based diagnosis (CAD) system,
focused on this feature in particular. The developed IBR and CAD algorithms start with automatic segmentation,
boundary and landmark detection. Then, shape and activity distribution features are computed. Activity distribution
feature is obtained using the acquired image and image set statistics of the normal patients. Active Shape Model (ASM)
technique is used for more accurate kidney segmentation. In the training step of ASM, normal patient images are used.
Retrieval performance is evaluated by calculating precision and recall. CAD performance is evaluated by specificity and
sensitivity. To our knowledge, this paper is the first IBR or CAD system reported in the literature on renal cortical