Application of Generalized Partial Volume Estimation for Mutual Information based Registration of High Resolution SAR and Optical Imagery

Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance has been thoroughly explored for registration of high resolution SAR (TerraSAR-X) and optical (IKONOS-2) satellite images. Our experiments highlight possibility of an inconsistent MI behavior with different joint histogram bin size which gets reduced with an increase in order of B-spline kernel employed in GPVE. In general, bin size reduction and/or increasing B-spline order have a smoothing affect on MI surfaces and even the lowest order B-spline with a suitable histogram bin size can achieve same pixel level accuracy as achieved by the higher order kernels more consistently

Impact of Behavioral Health Consultant Intervention on Health Outcomes in an Integrated Primary Care Setting

Integrated Primary Care Models are fast gaining popularity, and often being advocated as an important step in enhancing patient care within a primary care setting. Several studies have shown a positive impact of Behavioral Health Consultant (BHC) intervention in terms of patient satisfaction, improvement in patient symptom scores and global mental functioning. However, additional studies are needed to assess if integrated behavioral health consultations can demonstrate measurable outcome improvements to quantify the benefits of this approach, related to some of the most commonly seen and treated medical diseases in a primary care setting. This study aims to measure three outcomes: change in HbA1c levels, PHQ-9 scores (validated clinical scale for depression), and compliance rates for follow-up of patients in a primary care setting before and after BHC intervention and to determine if there is a statistically significant change related to these health outcomes

On the Possibility of Intensity Based Registration for Metric Resolution SAR and Optical Imagery

Multimodal image registration is a key to many remote sensing tasks like fusion, change detection, GIS overlay operations, 3D visualization etc. With advancements in research, intensity based similarity metrics namely mutual information (MI) and cluster reward algorithm (CRA) have been utilized for intricate multimodal registration problem. The computation of these metrics involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g. SAR and optical). Modern day satellites like TerraSAR-X and IKONOS provide high resolution images generating enormous data volume along with very different image radiometric properties (especially in urban areas) not observed ever before. Thus, performance evaluation of intensity based registration techniques for metric resolution imagery becomes an interesting case study. In this paper, we analyze the performance of similarity metrics namely, mutual information and cluster reward algorithm for metric resolution images acquired over both plain and urban/semi-urban areas. Techniques for handling the generated enormous data volume and influence of really different sensor geometries over images especially acquired over urban areas have also been proposed and rightfully analyzed. Our findings from three carefully selected datasets indicate that the intensity based techniques can still be utilized for high resolution imagery but certain adaptations (like compression and segmentation) become useful for meaningful registration results

Registration of High Resolution SAR and Optical Satellite Imagery in Urban Areas

With the launch of high resolution remote sensing satellites in different modalities like TerraSAR-X, WorldView-1 and Ikonos, the contribution of remote sensing for various applications has received a tremendous boost. Specifically, the combined analysis of high resolution SAR and optical imagery is of immense importance in monitoring and assessing catastrophes and natural disaster. Although, latest satellites provide georeferenced and orthorectified data products, still registration errors exist within images acquired from different sources. These need to be taken care off through quick automated techniques before the deployment of these data sources for remote sensing applications. Modern satellites like TerraSAR-X and Ikonos have further widened the existing gap of sensor geometry and radiometry between the two sensors. These satellites provide high resolution images generating enormous data volume along with very different image radiometric and geometric properties (especially in urban areas) leading to failure of multimodal similarity metrics like mutual information to detect the correct registration parameters. In this paper we present a processing chain to register high resolution SAR and optical images by combining feature based techniques namely, homogeneous regions extracted from high resolution images and intensity based similarity metrics namely normalized cross correlation and mutual information. Our test dataset consist of images from TerraSAR-X and Ikonos acquired over the city of Sichuan, China. First results from registration show good visual alignment of SAR and the optical image

Combining Mutual Information and Scale Invariant Feature Transform for Fast and Robust Multisensor SAR Image Registration

The Scale Invariant Feature Transform (SIFT) operator's success for computer vision applications makes it an attractive solution for the intricate feature based SAR image registration problem. For SAR images, SIFT feature matching results into lot of false alarms. To overcome the mentioned problem, we propose to use mutual information (MI) along with the SIFT operator for SAR image registration and matching applications. MI is an established multimodal registration similarity metric and has the capability to quickly estimate rough registration parameters from down-sampled images. The rough image registration parameters obtained using MI can be introduced for conjugate feature selection during the SIFT matching phase. Introduction of MI to the SIFT processing chain not only reduces the number of false alarms drastically but also helps to increase the number of matches as the operator detection and matching thresholds can be relaxed, relying on the available mutual information estimate. Further, the matching consistency of the SIFT matches especially for SAR images with various acquisition differences might not be up to the desired levels. To tackle the observed phenomenon, MI can further be utilized to refine the SIFT matches and to bring the matching consistency within desirable limits. We present our analysis based on multisensor, multitemporal and different view point SAR images acquired over plain and semi urban areas. The proposed registration methodology shows tremendous potential to become a fast and robust alternative for geometric SAR image registration as subpixel registration consistency has been achieved for diverse natured datasets

Modifications in the SIFT operator for effective SAR image matching

With the increasing availability and rapidly improving spatial resolution of SAR images from latest and future satellites like TerraSAR-X and TanDEM-X, their applicability in remote sensing applications is set to be paramount. Considering challenges in the field of point feature based multisensor/multimodal SAR image matching/registration and advancements in the field of computer vision, we extend the applicability of the Scale Invariant Feature Transform (SIFT) operator for SAR images. In this paper, we have analyzed the feature detection, identification and matching steps of the original SIFT processing chain. We implement steps to counter the speckle influence which deteriorates the SIFT operator performance for SAR images. In feature identification, we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFT’s rotation invariance characteristic increases the potential number of matches when the multiple SAR images from different sensors have been acquired with the same geometrical acquisition parameters. In the feature matching stage we propose to assist the standard SIFT matching scheme to utilize the SIFT operator capability for effective results in challenging SAR image matching scenarios. The results obtained for SAR images acquired by different sensors using different incidence angles and orbiting directions over both rural and semi urban land cover, highlight the SIFT operator’s capability for point feature matching in SAR imagery

Automatic Image to Image Registration for Multimodal Remote Sensing Images

During the last decades, remote sensing sensors have undergone a rapid development in terms of both data quantity and characteristics. With advancements in remote sensing technology, the use of satellite images in disparate fields has received a tremendous boost. Few of these include generation of 3D models and topographic maps, early warning systems, urban growth, damage assessment, crisis information management and disaster mitigation. These applications normally utilize image processing techniques like image fusion, change detection, GIS overlay operations or 3D visualization which requires registered images procured from different sources. Image registration is a fundamental task in remote sensing image processing that is used to match two or more images taken, for example, at different times, from different sensors or from different view points. A lot of automation has been achieved in this field but ever sprouting data quality and characteristics compel innovators to design new and/or improve existing registration techniques. In literature, image registration methodologies are broadly classified into intensity and feature based approaches. In this dissertation, we have evolved and combined two distinct techniques from each of the broad classes to extend their applicability for answering contemporary challenges in remote sensing image registration. Generally, remote sensing applications need to accommodate images from different sensors/modalities; reason might be specific application demands or data availability. For example in case of a natural calamity, decision makers might be forced to use old archived optical data with a newly acquired (post-disaster) SAR image. Misalignment within procured SAR and optical imagery (both orthorectified) in such scenarios is a common phenomenon and these registration differences need to be taken care of prior to their joint application. Considering the recently available very high resolution (VHR) data available from satellites like TerraSAR-X, Risat, IKONOS, Quickbird, ALOS etc, registering these images manually is a mammoth task (due to volume and scene characteristics). Intensity based similarity metrics like mutual information (MI) and cluster reward algorithm (CRA) have been found useful for achieving registration of SARoptical data from satellites like Landsat, Radarsat, SPOT, and IRS but still their application for high resolution data especially acquired over urban areas is limited. In this dissertation, we analyze in detail the performance of MI for very high resolution remote sensing images and evaluate (feature extraction, classification, segmentation, discrete optimization) for improving its accuracy, applicability and processing time for VHR images (mainly TerraSAR-X and IKONOS-2) acquired over dense urban areas. Further, on basis of the proposed modifications, we also present a novel method to improve the sensor orientation of high resolution optical data (IKONOS-2) by obtaining ground control through local image matching, taking geometrically much more accurate TerraSAR-X images as a reference. Apart from the joint application demands of SAR and optical imagery, the improved spatial resolution of SAR images from latest and future satellites like TerraSAR-X and TanDEM-X, is set to make a paramount impact on their usability. Here, the lack of any proven point feature detection and matching scheme for multisensor/multimodal SAR image matching encourages us to review the advancements in the field of computer vision and extend the applicability of Scale Invariant Feature Transform (SIFT) operator for SAR point feature matching. We have analysed the feature detection, identification and matching steps of the original SIFT processing chain. After thorough analysis, we propose steps to counter the speckle influence which deteriorates the SIFT operator performance for SAR images, in feature identification we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFT’s rotation invariance characteristic increases the potential number of matches. In the feature matching stage we propose to combine MI and the SIFT operator capabilities for effective results in challenging SAR image matching scenarios. Further, our results indicate that a significant speedup is achieved on incorporating above suggested changes to the original SIFT processing chain

Automatic Traffic Monitoring from an Airborne Wide Angle Camera System

We present an automatic traffic monitoring approach using data of an airborne wide angle camera system. This camera, namely the “3K-Camera”, was recently developed at the German Aerospace Center (DLR). It has a coverage of 8 km perpendicular to the flight direction at a flight height of 3000 m with a resolution of 45 cm and is capable to take images at a frame rate of up to 3 fps. Based on georeferenced images obtained from this camera system, a near real-time processing chain containing road extraction, vehicle detection, and vehicle tracking was developed and tested. The road extraction algorithms handle a-priori information provided by a road database for a first guess of the location of the roads. Two different techniques can be used for road extraction. In the first method, roadside features are found by using an edge detector based on ISEF filtering, selecting the steepest edge, which is normally the edge between the tarry roads and the vegetation. The second method extracts roads by searching the roadside markings using a dynamical threshold operator and a line detector. Vehicle detection then is limited to the road areas found by the road extraction algorithms. It is based on an edge detector, a k-means clustering of the edges, and on geometrical constraints, e.g. vehicle size. Vehicle tracking is performed by matching detected vehicles in pairs of consecutive images. For this matching the normalized cross correlation is calculated for each detected car within a limited search area. The algorithms for road extraction, vehicle detection and vehicle tracking proved to be quite sophisticated, enabling car detection and tracking rates with a completeness of 70 % and a correctness of up to 90 % on images obtained from a flight height of 1000 m

Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas

The launch of high-resolution remote sensing satellites like TerraSAR-X, WorldView, and Ikonos has benefited the combined application of synthetic aperture radar (SAR) and optical imageries tremendously. Specifically, in case of natural calamities or disasters, decision makers can now easily use an old archived optical with a newly acquired (postdisaster) SAR image. Although the latest satellites provide the end user already georeferenced and orthorectified data products, still, registration differences exist between different data sets. These differences need to be taken care of through quick automated registration techniques before using the images in different applications. Specifically, mutual information (MI) has been utilized for the intricate SAR–optical registration problem. The computation of this metric involves estimating the joint histogram directly from image intensity values, which might have been generated from different sensor geometries and/or modalities (e.g., SAR and optical). Satellites carrying high-resolution remote sensing sensors like TerraSAR-X and Ikonos generate enormous data volume along with fine Earth observation details that might lead to failure of MI to detect correct registration parameters. In this paper, a solely histogram-based method to achieve automatic registration within TerraSAR-X and Ikonos images acquired specifically over urban areas is analyzed. Taking future sensors into a perspective, techniques like compression and segmentation for handling the enormous data volume and incompatible radiometry generated due to different SAR–optical image acquisition characteristics have been rightfully analyzed. The findings indicate that the proposed method is successful in estimating large global shifts followed by a fine refinement of registration parameters for high-resolution images acquired over dense urban areas