Compressed domain classification of remote sensing scene images based on sub-band data fusion

Berk Arican; Erdem Safa Akkul; Levent Çarkacıoğlu; Behçet Uğur Töreyin

doi:10.1117/12.2635862

26 October 2022 Compressed domain classification of remote sensing scene images based on sub-band data fusion

Berk Arican , Erdem Safa Akkul, Levent Çarkacıoğlu, Behçet Uğur Töreyin

Proceedings Volume 12267, Image and Signal Processing for Remote Sensing XXVIII; 122670A (2022) https://doi.org/10.1117/12.2635862
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

Storing and processing Remote Sensing (RS) images require large amounts of memory space and computing resources. Consequently, RS images are compressed and stored in various compression formats, such as JPEG2000. However, the processing of RS images for machine interpretation and understanding still necessitates the deployment of an image decompression stage in its entirety, followed by a computationally demanding image analysis pipeline. The image analysis stage is commonly composed of machine learning techniques, such as Deep Convolutional Neural Network (DCNN) models. Classification of remote sensing images is among the most common image analysis tasks. In the scope of this paper, we propose a sub-band image based classification method for the Remote Sensing Scene Classification (RSSC) task in the JPEG2000 compressed domain. The proposed approach exploits the already available sub-band image coefficients to classify RS images without needing for full decompression. Our study shows that our method increases the high frequency information in the LL sub-band and allows the image to contain more detail, leading to improved classifier performance while taking advantage of the partial decompression method.

Citation Download Citation

Berk Arican , Erdem Safa Akkul, Levent Çarkacıoğlu, and Behçet Uğur Töreyin "Compressed domain classification of remote sensing scene images based on sub-band data fusion", Proc. SPIE 12267, Image and Signal Processing for Remote Sensing XXVIII, 122670A (26 October 2022); https://doi.org/10.1117/12.2635862

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available