Dr. Jasmin Lampert Profile

Dr. Jasmin Lampert

at AIT Austrian Institute of Technology GmbH

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 26 October 2022 Presentation + Paper

Remote sensing image classification using transfer learning and attention-based deep neural network

Lam Pham, Khoa Tran, Dat Ngo, Jasmin Lampert, Alexander Schindler

Proceedings Volume 12267, 122670J (2022) https://doi.org/10.1117/12.2635320

KEYWORDS: Neural networks, Network architectures, Image classification, Remote sensing, Image processing, Feature extraction, Data fusion

Read Abstract +

The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has assumed an important role in a wide range of applications such as urban planning, natural hazards detection, environmental monitoring, vegetation mapping or geospatial object detection. During the past years, the research community focusing on RSISC tasks has shown significant effort to publish diverse datasets as well as to propose different approaches. Recently, almost all proposed RSISC systems are based on deep learning models, which proves powerful and outperform traditional approaches using image processing and machine learning. In this paper, we also leverage the power of deep learning technologies, evaluate a variety of deep neural network architectures and indicate main factors affecting the performance of a RSISC system. Given the comprehensive analysis, we propose a deep learning based framework for RSISC, which makes use of a transfer learning technique and a multihead attention scheme. The proposed deep learning framework is evaluated on the NWPU-RESISC45 benchmark dataset and achieves a classification accuracy of up to 92.6% and 94.7% with two official data split suggestions (10% and 20% of entire the NWPU-RESISC45 dataset for training). The achieved results are very competitive and show potential for real-life applications.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years