SZTAKI DEVA in Remote Sensing, Pattern recognition and change detection In Remote sensing Distributed Events Analysis Research Group Computer and Automation Research Institute Hungarian Academy of Sciences

SZTAKI DEVA in Remote Sensing, Foreground separation and object segmentation on aerial images (1/2) Camera on an airborne vehicle: UAV, airplane, helicopter, or balloon Goal: Object detection (and tracking) Application: Military operations, traffic monitoring, surveillance Operating in near-real time (5 fps on 640x480 frames) Frame mosaic Background subtraction Global imageForeground mask Moving object detection Object assignment and tracking Objects

SZTAKI DEVA in Remote Sensing, Change detection over long time frames (1/2) CXM: region level change detection Large (many years) time differences ? different seasons, illumination conditions, vegetations etc. Proposed Conditional MiXed Markov Model (CXM): featuring relevant changes versus irrelevant differences in remotely sensed image pairs: new built-up regions, building operations, planting of trees, fresh plough-land, groundwork before building-over etc. Input – preliminary registered orthophotos: Image 1 from 2000Image 2 from 2005

SZTAKI DEVA in Remote Sensing, Change detection over long time frames (2/2) CXM results & comparison to conventional frame differencing Ground Truth change mask Change mask overlaid by thresholded difference image Proposed CXM result Image 1 Image 2

SZTAKI DEVA in Remote Sensing, Segmentation of buildings on aerial images (1/6) Single image mode: Input: single grayscale or color image Output: size, position and orientation parameters of the detected houses/house parts Multi-temporal mode: Input: pair of co-registered grayscale or color images, taken at different time instances Output: in each image we provide the size, position and orientation parameters of the detected houses/house parts, with giving information which objects are new, demolished, modified and unchanged

SZTAKI DEVA in Remote Sensing, Segmentation of buildings on aerial images (3/6) Results in dense suburban areas Input image Detection result

SZTAKI DEVA in Remote Sensing, Segmentation of buildings on aerial images (4/6) Joint building & change detection Input image 1 Input image 2 Detection result img. 2 Detection result img. 1 Change mask changed, new or demolished building unchanged building

SZTAKI DEVA in Remote Sensing, Segmentation of buildings on aerial images (6/6) Real-time building detection from UAV video sequences Raw video Roof filter Roof contourPoligon approximation of the contour Video provided by UNIBWM

SZTAKI DEVA in Remote Sensing, Segmentation of structural changes of irregular shapes in long time-span aerial image samples Harris saliency function Key points Local descriptor by local active contours Graph based shape descriptor

SZTAKI DEVA in Remote Sensing, Change detection with Harris keypoints saliency points: local maximas of R filtering keypoint candidates with local contours enhancing the number of keypoints

SZTAKI DEVA in Remote Sensing, Change detection with Harris keypoints How to determine points belonging to the same object? Graph-based representation (Sirmacek, IEEE Tr. Geo. Sci. And R. Sens.) edge network: according to Canny edge maps (R and u* components) Contour detection based on the resulted subgraphs

SZTAKI DEVA in Remote Sensing, Selected DEVA Publications in Remote Sensing see more in: A. Kovács, T. Szirányi, ”New saliency point detection and evaluation methods for finding structural differences in remote sensing images of long time-span samples”, ACIVS 2010: Advanced Concepts for Intelligent Vision Systems, Sydney, 2010 G. Máttyus, Cs. Benedek, T. Szirányi, ”Multi target tracking on aerial videos”, ISPRS Workshop 2010: Modeling of optical airborne and space borne sensors, Istanbul, 2010 R. Gaetano, G. Scarpa, T. Szirányi, ”Graph-based Analysis of Textured Images for Hierarchical Segmentation”, BMVC 2010: British Machine Vision Conference, Paper 318, 2010 Cs. Benedek, X. Descombes and J. Zerubia, ”Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles,” Int. Conference on Pattern Recognition (ICPR), Istanbul, Turkey, 2010 Cs. Benedek, ”Efficient Building Change Detection in Sparsely Populated Areas Using Coupled Marked Point Processes,” IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Honolulu, Hawaii, 2010 Cs. Benedek, T. Szirányi, ”Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model”, IEEE Tr. Geoscience and Remote Sensing, V. 47(10), October 2009 L. Havasi, Z. Szlávik, T. Szirányi, “ The Use of Vanishing Point for the Classification of Reflections from Foreground Mask in Videos”, IEEE Tr. Image Processing, 2009 Cs. Benedek, T. Szirányi, Z. Kató, J. Zerubia, ”Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model”, IEEE Tr. Image Processing, in press, 2009 Cs. Benedek, T. Szirányi, ”Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos”, IEEE Tr. Image Processing, V.17, No.4, pp , 2008 Z. Szlávik, T. Szirányi, L. Havasi, ”Video camera registration using accumulated co-motion maps”, ISPRS J Photogrammetry and Remote Sensing, V.61(1), pp , 2007 L. Kovács, T. Szirányi, ”Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest”, IEEE Tr. Pattern Analysis and Machine Intelligence, V.29, No.6, pp , 2007 Z. Szlávik, T. Szirányi, L. Havasi, ”Stochastic view registration of overlapping cameras based on arbitrary motion”, IEEE Tr. Image Processing, Vol.16, No.3, pp , 2007