Classification of remote sensed images using random forests and deep learning framework

S. Piramanayagam; W. Schwartzkopf; F. W. Koehler; E. Saber

doi:10.1117/12.2243169

18 October 2016 Classification of remote sensed images using random forests and deep learning framework

S. Piramanayagam, W. Schwartzkopf, F. W. Koehler, E. Saber

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100040L (2016) https://doi.org/10.1117/12.2243169
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

In this paper, we explore the use of two machine learning algorithms: (a) random forest for structured labels and (b) fully convolutional neural network for the land cover classification of multi-sensor remote sensed images. In random forest algorithm, individual decision trees are trained on features obtained from image patches and corresponding patch labels. Structural information present in the image patches improves the classification performance when compared to just utilizing pixel features. Random forest method was trained and evaluated on the ISPRS Vaihingen dataset that consist of true ortho photo (TOP: near IR, R, G) and Digital Surface Model (DSM) data. The method achieves an overall accuracy of 86.3% on the test dataset. We also show qualitative results on a SAR image. In addition, we employ a fully convolutional neural network framework (FCN) to do pixel-wise classification of the above multi-sensor image. TOP and DSM data have individual convolutional layers with features fused before the fully convolutional layers. The network when evaluated on the Vaihingen dataset achieves an overall classification accuracy of 88%.

Conference Presentation

Citation Download Citation

S. Piramanayagam, W. Schwartzkopf, F. W. Koehler, and E. Saber "Classification of remote sensed images using random forests and deep learning framework", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040L (18 October 2016); https://doi.org/10.1117/12.2243169

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