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Main Title: A Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classification
Author(s): Sumbul, Gencer
Demir, Begüm
Type: Conference Object
Language Code: en
Abstract: This paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed system consists of four main modules. The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial resolutions. To this end, we introduce a K-Branch CNN, in which each branch extracts descriptors of image bands that have the same spatial resolution. The second module aims to model spatial relationship among local descriptors. This is achieved by a bidirectional RNN architecture, in which Long Short-Term Memory nodes enrich local descriptors by considering spatial relationships of local areas (image patches). The third module aims to define multiple attention scores for local descriptors. This is achieved by a novel patch-based multi-attention mechanism that takes into account the joint occurrence of multiple land-cover classes and provides the attention-based local descriptors. The last module exploits these descriptors for multi-label RS image classification. Experimental results obtained on the BigEarth-Net that is a large-scale Sentinel-2 benchmark archive show the effectiveness of the proposed method compared to a state of the art method.
Issue Date: 14-Nov-2019
Date Available: 25-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): multi-label image classification
deep neural network
attention mechanism
remote sensing
Sponsor/Funder: EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation Fact Sheet/BigEarth
Proceedings Title: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/IGARSS.2019.8898188
Page Start: 5726
Page End: 5729
EISSN: 2153-7003
ISBN: 978-1-5386-9154-0
ISSN: 2153-6996
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Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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