Serial distributed detection strategies for wireless sensor networks

The interest in wireless sensor networks (WSNs) has been significantly increased over the past years due to their promising future in wide range of application areas like health, military and home. Various technical issues of WSNs have been investigated recently such as topology management, efficient routing protocols and collaborative signal processing. This thesis considers serial distributed detection in WSNs. Unlike traditional distributed detection algorithms where error-free transmissions of local decisions to the fusion center are assumed, lossless communication is not applicable in WSNs since wireless transmission channels are subjected to fading and interference. Suggested distributed detection algorithms in WSNs should deal with the channel uncertainty due to fading and noisy effects of non-ideal channel under low power transmission. In this thesis, we first propose suboptimal fusion rules to the optimal fusion rule for serial distributed detection. In particular, we derive the low and high SNR approximations of the optimal rule in order to relieve some requirements of the optimal fusion rule. Then, we investigate effects of node failure to the serial distributed detection performance and we suggest more robust decision fusion rules under node failure. Lastly, we analyze effects of decision feedback at serial network topology. In order to improve serial distributed detection performance we propose feedback strategies and derive appropriate decision fusion rules for suggested strategies

Disaster damage assessment of buildings using adaptive self-similarity descriptor

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.This work was supported in part by the Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency (AFAD) and TUBITAK BILGEM under Grant B740-G585000Publisher's Versio

Battle damage assessment based on self-similarity and contextual modeling of buildings in dense urban areas

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.This research was supported in part by Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency (AFAD) and TUBITAK BILGEM under Grants B740-G585000Publisher's Versio

Disaster damage assessment for buildings using self-similarity descriptor

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.Publisher's Versio

VISKON-RS: Rapid damage assessment software with remote sensing

Afet sonrası, acil müdahale ekiplerinin yönlendirilmesi ve iyileştirme çalışmalarının planlanması amacıyla hızlı hasar değerlendirmesine ihtiyaç duyulmaktadır. Bu çalışmada, AFAD ihtiyaçları doğrultusunda uzaktan (uzay/hava) algılama teknolojileri ile elde edilen görüntülerin afet hasar analizinde kullanılmasına yönelik VİSKON-RS yazılımı geliştirilmiştir. Geliştirilen yazılım; deprem, sel ve orman yangını gibi afet türlerinin hasar analizlerine özelleşmiş uygulamalar içermektedir. Ayrıca genel görüntü analizinde kullanılabilecek değişiklik analizi, eğitimli/eğitimsiz sınıflandırma, nesne tabanlı görüntü analizi ve doku analizi gibi uygulamalar yazılıma entegre edilmiştir. VİSKON-RS yazılımın temel amacı, afet sonrası uzaktan algılama verilerinin, açılması, işlenmesi, analiz edilmesi, sonuçların sergilenmesi ve karar destek sistemlerine aktarılması adımlarını içeren bütünleşik bir yazılım çözümü sunmaktır.After a disaster, a rapid damage assessment is required for coordinating emergency response teams and planning emergency aid. In this study, in line with AFAD requirements, ViSKON-RS software was developed for the aim of using disaster damage assessment by analysing images obtained via remote(space/air) imaging technologies. The developed software includes specialized applications for damage assessment of disaster types such as earthquake, flood and forest fires. In addition, applications for general image analysis were integrated to the software like change detection analysis, supervised/unsupervised classification, object based image analysis and texture analysis. The main purpose of ViSKON-RS software is to be integrated software solution by opening, processing, analysing, showing and exporting results to desicion support system of post-disaster remote sensing data.Publisher's Versio