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Main Title: Approximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classification
Author(s): Preethy Byju, Akshara
Sumbul, Gencer
Demir, Begüm
Bruzzone, Lorenzo
Type: Conference Object
Language Code: en
Abstract: This paper presents a novel approach based on the direct use of deep neural networks to approximate wavelet sub-bands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that utilizes the coarser level binary decoded wavelet sub-bands to approximate the finer level wavelet sub-bands (the image itself) through a series of deconvolutional layers. The second step aims to describe the high-level semantic content of the approximated wavelet sub- bands and to perform scene classification based on the learnt descriptors. This is achieved by: i) a series of convolutional layers for the extraction of descriptors which models the approximated sub-bands; and ii) fully connected layers for the RS image scene classification. Then, we introduce a loss function that allows to learn the parameters of both steps in an end-to-end trainable and unified neural network. The proposed approach requires only the coarser level wavelet sub-bands as input and thus minimizes the amount of decompression applied to the compressed RS images. Experimental results show the effectiveness of the proposed approach in terms of classification accuracy and reduced computational time when compared to the conventional use of Convolutional Neural Networks within the JPEG 2000 compressed domain.
Issue Date: 15-Oct-2019
Date Available: 25-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): scene classification in compressed domain
JPEG 2000
deep neural network
remote sensing
Proceedings Title: Proceedings of SPIE 11155 – Image and Signal Processing for Remote Sensing XXV
Editor: Bruzzone, Lorenzo
Bovolo, Francesca
Publisher: SPIE
Publisher Place: Bellingham, Wash.
Article Number: 111550S
Publisher DOI: 10.1117/12.2534643
EISSN: 1996-756X
ISBN: 9781510630147
ISSN: 0277-786X
Notes: Copyright 2019 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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