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Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering

16 Sep 2013IEEE Transactions on Geoscience and Remote Sensing 2013 9archive 2025-07-28

Xudong Kang, Shutao Li, Jón Atli Benediktsson

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.

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Tasks

Classification Computational Efficiency Few-Shot Image Classification Hyperspectral Image Classification Image Classification image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Indian Pines	IFRF	OA@15perclass	69.52	#9 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Kennedy Space Center	IFRF	OA@15perclass	95.07	#6 of 14	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	IFRF	OA@15perclass	88.38	#5 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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