DocumentCode :
3070239
Title :
Sequential cascade classification of image time series by exploiting multiple pairwise change detection
Author :
Demir, Begum ; Bovolo, Francesca ; Bruzzone, Lorenzo
Author_Institution :
Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
3946
Lastpage :
3949
Abstract :
This paper presents a novel sequential cascade classification technique for automatically updating land-cover maps by classifying remote sensing image time series. We assume that a reliable training set is initially available only for one of the images (i.e., the source domain) in the time series, whereas it is not for an image being classified (i.e., the target domain). Unlike the standard cascade classification method, the proposed method aims at exploiting all the images in the time series acquired between the target and source domains to effectively classify the target domain. To this end, initially `pseudo´ training sets of the images are defined by a multiple pairwise change detection based transfer learning strategy. Then, the target domain is classified by the proposed sequential cascade classification method, exploiting the temporal correlation between images. Experimental results obtained on a time series of Landsat multispectral images show the effectiveness of the proposed technique with respect to the standard cascade classification.
Keywords :
correlation methods; geophysical image processing; image classification; land cover; learning (artificial intelligence); object detection; terrain mapping; time series; Landsat multispectral images; automatical land cover maps updating; multiple pairwise change detection; pseudo training sets; reliable training set; remote sensing image time series classification; sequential cascade classification technique; source domain; standard cascade classification method; target domain classification; temporal correlation; transfer learning strategy; Accuracy; Correlation; Reliability; Remote sensing; Standards; Time series analysis; Training; multiple pairwise change detection; remote sensing; sequential cascade classification; time series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6723696
Filename :
6723696
Link To Document :
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