Video Mosaicing of Planer Scenes using Extended Kalman Filter

In this work, the large planar scene is reconstructed from small images. Small images can be consecutive video frames or sequence of photographs. In the problem, called mosaicing, instead of using widely used optimization based methods, probabilistic methods are used in the proposed method. Simultaneous Localization And Mapping(SLAM) techniques are adapted for video mosaicing. Probabilistic measures for the landmark locations are used to merge small images to create large scene. Experimental tests give promising results if the performance-complexity is considered at the same time

SUPERPIXEL BASED HYPERSPECTRAL TARGET DETECTION

Using the spectral signature of a target by means of matching the signature with the pixels of an acquired hyperspectral image has been proven as an effective way of classifying hyperspectral pixels in most of the proposed methods in hyperspectral image analysis. A disadvantage of these methods is however to use only the spectral characteristics of pixels for detection while ignoring the spatial relations between the neighbouring pixels. In this paper, we propose a hyperspectral target detection method which uses also the spatial neigboorhood information as well as the spectral characteristics of hyperspectral pixels. To this end, we first utilize superpixelization method [1] to describe the neigborhood relation between the hyperspectral pixels, which has been previously developed and proved to be better compared to a pioneer state-of-the-art superpixel algorithm, SLIC [2]. Second, we investigate the best representatives for superpixels among different alternatives, such as centroids, medoid and mean, and modify the well-known hyperspectral target detection algorithm using orthogonal subspace projection, DTDCA [3], appropriately for superpixels. The improvements of the proposed approach over DTDCA in terms of the detection and false detection rates are verified on real hyperspectral images taken from wheat and corn fields with a VNIR camera

Circular Target Detection Algorithm on Satellite Images based on Radial Transformation

Remote sensing is used in a spreading manner by many governmental and industrial institutions worldwide in recent years. Target detection has an important place among the applications developed using satellite imagery. In this paper, an original circular target detection algorithm has been proposed based on a radial transformation. The algorithm consists of three stages such as pre-processing, target detection, and post-processing. In the pre-processing stage, bilateral noise reduction filtering and vegetation detection operations are completed which they are required by target detection step. The target detection stage finds the circular target by a radial transformation algorithm and variables obtained from the training, and post-processing stage carries out the elimination of falsely detected targets by utilizing the vegetation information. The Petroleum Oil Lubricants (POL) depots in the industrial areas and harbors have been chosen as an application area of the proposed algorithm. The algorithm has been trained and tested on a data set which includes 4-band images with Near-Infrared band. Proposed algorithm is able to detect many circular targets with different types and sizes as a consequence of using a full radial transformation search as well as it gives rewarding results on industrial areas and harbors in the experiments conducted

Circular target detection algorithm on satellite images based on radial transformation

Remote sensing is used in a spreading manner by many governmental and industrial institutions worldwide in recent years. Target detection has an important place among the applications developed using satellite imagery. In this paper, an original circular target detection algorithm has been proposed based on a radial transformation. The algorithm consists of three stages such as pre-processing, target detection, and post-processing. In the pre-processing stage, bilateral noise reduction filtering and vegetation detection operations are completed which they are required by target detection step. The target detection stage finds the circular target by a radial transformation algorithm and variables obtained from the training, and post-processing stage carries out the elimination of falsely detected targets by utilizing the vegetation information. The Petroleum Oil Lubricants (POL) depots in the industrial areas and harbors have been chosen as an application area of the proposed algorithm. The algorithm has been trained and tested on a data set which includes 4-band images with Near-Infrared band. Proposed algorithm is able to detect many circular targets with different types and sizes as a consequence of using a full radial transformation search as well as it gives rewarding results on industrial areas and harbors in the experiments conducted

Anomaly Based Target Detection in Hyperspectral Images via Graph Cuts

The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, background and neighbouring information and improved the detection performance by locating the target as a whole object free from noises

Uydu Görüntülerinin Otomatik Analizi ile Afet Hasar Tespiti ve Kanunsuz Sınır Geçişlerinin Önlenmesi

Proje önerisinin temel amacı ODTÜ Görüntü Analizi Uygulama ve Araştırma Merkezi (OGAM) bünyesinde yer alan araştırıcıların geçmişte HAVELSAN altyükleniciliğinde gerçekleştirdikleri savunma ile ilgili SSM destekli HASAT projesi deneyim ve sonuçlarının, Avrupa Birliği H2020 Programı Uzay Alanı ana başlıkları ve çağrı alanlarına uygulanacak şekilde kurgulanmasıdır. Bu kapsamda, HASAT projesi kapsamında uydu görüntüleri içinde yer alan ve otomatik olarak tanınması için ayrı ayrı çalışmalar yürütülmüş istihbarat hedeflerinin bir kısmı kullanılarak, H2020 Programı Uzay Alanı amaç ve hedefleri doğrultusunda bu hedefler bir arada değerlendirilip, bir yazılım arayüzü altında biraraya getirilip, hedeflenen uygulamalar için otomatik tanıma çözümleri yaratılacaktır.Bu amaçla yapılacak çalışmalar, ilgili deneyim ve birikimin OGAM bünyesinde kalıcı olması, sivil uygulamalara yönelik yeni uluslararası proje imkanları yaratması ve HASAT projesindeki otomatik tanıma çalışma sonuçlarının, hedeflenen yeni uygulamalara ait kıstasları da dikkate alarak ve farklı tanıma sonuçlarını birarada kullanarak daha ileriye götürülebilecek olması açılarından önemlidir