1 December 2017 Comparative analysis of feature extraction methods in satellite imagery
Author Affiliations +
J. of Applied Remote Sensing, 11(4), 042618 (2017). doi:10.1117/1.JRS.11.042618
Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shahid Karim, Ye Zhang, Muhammad Rizwan Asif, Saad Ali, "Comparative analysis of feature extraction methods in satellite imagery," Journal of Applied Remote Sensing 11(4), 042618 (1 December 2017). https://doi.org/10.1117/1.JRS.11.042618 Submission: Received 16 May 2017; Accepted 10 November 2017
Submission: Received 16 May 2017; Accepted 10 November 2017

Feature extraction

Earth observing sensors

Satellite imaging


Binary data


Detection and tracking algorithms

Back to Top