Comparative analysis of feature extraction methods in satellite imagery

Shahid Karim, Ye Zhang, Muhammad Rizwan Asif, Saad Ali

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


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.

Original languageEnglish
Article number042618
JournalJournal of Applied Remote Sensing
Publication statusPublished - 1 Dec 2017
Externally publishedYes


  • Feature extraction
  • feature matching
  • remote sensing
  • satellite imagery
  • shadow regions


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