Multi-temporal differential synthetic aperture radar interferometry techniques are well-developed and beneficial tools for deformation analysis using remotely sensed images. The primary objective of the study is to investigate the capability and the performance of the new generation SAR system Sentinel-1 data to detect and monitor surface deformation. We combined open source tools of Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for data processing. The results highlight the recent surface displacement rates over the Istanbul city. The deformation rates reach up to approximately 8 mm/yr over a 4 year period.

Assessment of battle damages is significant both for tactical planning and for after-war relief efforts. In this study damaged buildings are detected using self-similarity descriptor in pre- and post-war satellite images. Detection accuracy is improved by the use of a contextual model that describes the building neighborhoods. Building footprints are utilized for accurate assessment of building-level changes and for the formation of neighborhood context. The Gaza Strip after 2014 Israel-Palestine conflict is analyzed with the suggested method and 84% true positive rate and 19% false positive rate are obtained on the average for detection of damaged buildings with respect to the ground truth data of UNOSAT.

Assessment of damage caused by a disaster is significant for coordinating emergency response teams and planning emergency aid. In this letter, a robust method for rapid building damage assessment is proposed using pre- and postevent EO images and building footprints. The method uses a local self-similarity descriptor (SSD) for change detection in buildings, which is shown to be robust against variations in global illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in nonbuilding areas. Footprint is also used to differentiate small and large buildings, extract the boundary region of a building, and adapt the descriptor computation accordingly. It is shown that the adaptive SSD provides a more accurate measure of local damage on the building. The 2010 Haiti Earthquake and Typhoon Haiyan 2013 Philippines are analyzed with the proposed method, and 75/82% true positive rate and 25/15% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT and HOT.

Assessment of damage caused by an earthquake is significant for coordinating emergency response teams and planning emergency aid. In this study, a robust method is proposed for detecting damaged buildings using pre- and post-event satellite images and building footprints. The method uses local self-similarity descriptor for change detection in buildings, which is shown to be robust against variations in illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in non-building areas. The 2010 Haiti earthquake is analyzed with the suggested method and 72% true positive rate and 29% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT.

Face recognition systems are typically required to work under highly varying illumination conditions. This leads to complex effects imposed on the acquired face image that pertains little to the actual identity. Consequently, illumination normalization is required to reach acceptable recognition rates in face recognition systems. In this paper, we propose an approach that integrates the face identity and illumination models under the widely used active appearance model framework as an extension to the texture model in order to obtain illumination-invariant face localization.

In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set. The modified AAM search can now handle both illumination and pose variations in the same epoch, hence it provides better convergence in both point-to-point and point-to-curve senses. We also investigate how face recognition performance is affected by the selection of feature space as well as the proposed alignment method. The experimental results show that the combined pose alignment and illumination normalization methods increase the recognition rates considerably for all feature-spaces.

Localization of facial components is very important for many pattern recognition and computer vision applications such as face recognition, tracking of face expressions. This paper addresses the problem of precisely finding facial components, such as the eyes, the mouth, the nose etc. We present an illumination invariant three-stage approach for face alignment. This paper combines the concept of component-based approach and face alignment and develops component-based active appearance model (AAM) method for fine face alignment. We propose a new image representation to improve AAM segmentation accuracy for illumination invariant face alignment.