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Spatiotemporal Scene Interpretation of Space Videos via Deep Neural Network and Tracklet Analysis

Mou, Lichao and Zhu, Xiao Xiang (2016) Spatiotemporal Scene Interpretation of Space Videos via Deep Neural Network and Tracklet Analysis. IGARSS 2016, 10.-15.7.2016, Beijing, China.

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Spaceborne remote sensing videos are becoming indispensable resources, opening up opportunities for new remote sensing applications. To exploit this new type of data, we need sophisticated algorithms for semantic Scene interpretation. The main difficulties are: 1) Due to the relatively poor spatial resolution of the video acquired from space, moving objects, like cars, are very difficult to detect, not to mention track; 2) camera movement handicaps scene interpretation. To address these challenges, in this paper we propose a novel framework that fuses multispectral images and space videos for spatiotemporal analysis. Taking a multispectral image and a spaceborne video as input, an innovative deep neural network is proposed to fuse them in order to achieve a fine-resolution spatial scene labeling map. Moreover, a sophisticated approach is proposed to analyze activities and estimate traffic density from 150, 000+ tracklets produced by a Kanade-Lucas-Tomasi keypoint tracker. The proposed framework is validated using data provided for the 2016 IEEE GRSS data fusion contest, including a video acquired from the International Space Station and a DEIMOS-2 multispectral image. Both visual and quantitative analysis of the experimental results demonstrates the effectiveness of our approach.

Item URL in elib:https://elib.dlr.de/111271/
Document Type:Conference or Workshop Item (Speech)
Title:Spatiotemporal Scene Interpretation of Space Videos via Deep Neural Network and Tracklet Analysis
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangdlr-imf/tum-lmfUNSPECIFIED
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1823-1826
Keywords:space videos, scene labeling, deep learning, activity analysis, traffic density estimation
Event Title:IGARSS 2016
Event Location:Beijing, China
Event Type:international Conference
Event Dates:10.-15.7.2016
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:24 Feb 2017 10:52
Last Modified:24 Feb 2017 10:52

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