000162502 001__ 162502
000162502 005__ 20190316235022.0
000162502 037__ $$aSTUDENT
000162502 245__ $$aRegistration of multi-modal and multi-temporal remote sensing images and introduction to novelty detection
000162502 269__ $$a2009
000162502 260__ $$c2009
000162502 336__ $$aStudent Projects
000162502 520__ $$aThe database of satellite images covering the earth is growing extremely quickly and represents an important amount of data. The extension of this huge amount of data with images from very different sources and time of acquisition can provide a wide range of applications. The climate change monitoring could be more easily achieved or Natural disasters (deforestation, floods, dry rivers, earthquake) characterized by Unmanned Aerial Vehicle (UAV), allowing a rapid intervention. This project aims to realize a robust registration of images from different modalities (view angle, resolution, sensors sensitivity) having temporal changes and possible "novelties". A novelty would be a change not due to normal season changes, like with new buildings or natural disaster. An introduction to the application of novelty detection on multi-modal and multi-temporal images is discussed based on recent research on novelty detection. Video recordings from UAV are provided by RUAG. Different flight conditions have been realized to get a wide range of images, in terms of content, view angle, field of view or stabilization. The images, taken as reference, are from satellite (SPOT 5) and airborne (Google Map) sources. A registration algorithm based on the phase-correlation allowing to retrieve rotation, scaling and translation between two images is proposed. It handles the multi-modal characteristics of the images by correcting the perspective deformation, exploiting the redundancy of video sequence and adaptively choose between the image edges or the extraction of large structure from the image depending on the image frequency content. The results are encouraging for the possibility of novelty detection. The algorithm is sensitive to the initial conditions but accurate registration with stabilized and unstabilized flights is achieved using Google Maps. Less precise registration is achieved with SPOT images coming from their lower resolution.
000162502 6531_ $$aLTS5
000162502 6531_ $$aImage registration
000162502 6531_ $$aMulti-sources
000162502 6531_ $$aPhase-correlation
000162502 6531_ $$aNovelty detection
000162502 6531_ $$aAirborne video
000162502 6531_ $$aSatellite image
000162502 700__ $$ade Morsier, Frank
000162502 720_2 $$aCharrier, Rémi$$edir.
000162502 720_2 $$aThiran, Jean-Philippe$$edir.$$g115534$$0240323
000162502 8564_ $$uhttps://infoscience.epfl.ch/record/162502/files/Report.pdf$$zn/a$$s55232446$$yn/a
000162502 8564_ $$uhttps://infoscience.epfl.ch/record/162502/files/Report_lowres.pdf$$zn/a$$s18413891$$yn/a
000162502 909C0 $$xU10954$$0252394$$pLTS5
000162502 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:162502
000162502 917Z8 $$x166738
000162502 917Z8 $$x148230
000162502 917Z8 $$x148230
000162502 937__ $$aEPFL-STUDENT-162502
000162502 973__ $$sPUBLISHED$$aEPFL
000162502 980__ $$bMASTERS$$aSTUDENT