Feature extraction and descriptor calculation methods for automatic georeferencing  of Philippines' first microsatellite imagery

M. E. A. Tupas; J. A. Dasallas; B. J. D. Jiao; B. J. P. Magallon; J. N. H. Sempio; M. K. F. Ramos; R. K. D. Aranas; A. M. Tamondong

doi:10.1117/12.2278177

10 October 2017 Feature extraction and descriptor calculation methods for automatic georeferencing of Philippines' first microsatellite imagery

M. E. A. Tupas, J. A. Dasallas, B. J. D. Jiao, B. J. P. Magallon, J. N. H. Sempio, M. K. F. Ramos, R. K. D. Aranas, A. M. Tamondong

Author Affiliations +

Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 104271M (2017) https://doi.org/10.1117/12.2278177
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

The FAST-SIFT corner detector and descriptor extractor combination was used to automatically georeference DIWATA-1 Spaceborne Multispectral Imager images. Features from the Fast Accelerated Segment Test (FAST) algorithm detects corners or keypoints in an image, and these robustly detected keypoints have well-defined positions. Descriptors were computed using Scale-Invariant Feature Transform (SIFT) extractor. FAST-SIFT method effectively SMI same-subscene images detected by the NIR sensor. The method was also tested in stitching NIR images with varying subscene swept by the camera. The slave images were matched to the master image. The keypoints served as the ground control points. Random sample consensus was used to eliminate fall-out matches and ensure accuracy of the feature points from which the transformation parameters were derived. Keypoints are matched based on their descriptor vector. Nearest-neighbor matching is employed based on a metric distance between the descriptors. The metrics include Euclidean and city block, among others. Rough matching outputs not only the correct matches but also the faulty matches. A previous work in automatic georeferencing incorporates a geometric restriction. In this work, we applied a simplified version of the learning method. RANSAC was used to eliminate fall-out matches and ensure accuracy of the feature points. This method identifies if a point fits the transformation function and returns inlier matches. The transformation matrix was solved by Affine, Projective, and Polynomial models. The accuracy of the automatic georeferencing method were determined by calculating the RMSE of interest points, selected randomly, between the master image and transformed slave image.

Citation Download Citation

M. E. A. Tupas, J. A. Dasallas, B. J. D. Jiao, B. J. P. Magallon, J. N. H. Sempio, M. K. F. Ramos, R. K. D. Aranas, and A. M. Tamondong "Feature extraction and descriptor calculation methods for automatic georeferencing of Philippines' first microsatellite imagery", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271M (10 October 2017); https://doi.org/10.1117/12.2278177

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Georeferencing

Earth observing sensors

Landsat

Feature extraction

Sensors

Image segmentation

Near infrared

Show All Keywords

Keywords/Phrases

Search In:

Publication Years