Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2012
Öğrenci: MUSTAFA ERGÜL
Danışman: ABDULLAH AYDIN ALATAN
Özet:A great number of methods based on local features or global appearances have been proposed in the literature for geospatial object detection and recognition from satellite images. However, since these approaches do not have enough discriminative capabilities between object and non-object classes, they produce results with innumerable false positives during their detection process. Moreover, due to the sliding window mechanisms, these algorithms cannot yield exact location information for the detected objects. Therefore, a geospatial object recognition algorithm based on the object shape mask is proposed to minimize the aforementioned imperfections. In order to develop such a robust recognition system, foreground extraction performance of some of popular fully and semi-automatic image segmentation algorithms, such as normalized cut, k-means clustering, mean-shift for fully automatic, and interactive Graph-cut, GrowCut, GrabCut for semi-automatic, are evaluated in terms of their subjective and objective qualities. After this evaluation, the retrieval performance of some shape description techniques, such as ART, Hu moments and Fourier descriptors, are investigated quantitatively. In the proposed system, first of all, some hypothesis points are generated for a given test image. Then, the foreground extraction operation is achieved via GrabCut algorithm after utilizing these hypothesis points as if these are user inputs. Next, the extracted binary object masks are described by means of the integrated versions of shape description techniques. Afterwards, SVM classifier is used to identify the target objects. Finally, elimination of the multiple detections coming from the generation of hypothesis points is performed by some simple post-processing on the resultant masks. Experimental results reveal that the proposed algorithm has promising results in terms of accuracy in recognizing many geospatial objects, such as airplane and ship, from high resolution satellite imagery.