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Multi-Sensor Image Feature Fusion via Subspace-Based Approach Using l1-Gradient Regularization
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DOI：10.1109/JSTSP.2022.3219357.png)
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Image fusion is a technique of combining two ormore images into a single image which is more informative from an interpretation point of view. With the rapid development of different synthetic aperture radar sensing satellites capturing information from the earth by measuring energy in different portions of the electromagnetic spectrum (narrow/wide-band), complementary information about the area captured by different satellites is available (e.g. high-resolution spectral and RGB images). However, the estimation of the full-resolution image may not be necessary for inference approaches, including the pixel-based classification. Instead, it is desirable to extract the relevant information embedded in the available data to improve the inference capabilities. This work proposes a computational framework to estimate features with high-spatial-resolution and appropriate spectral content by combining information froma multi-sensor system. The considered multi-sensor setup is a hyperspectral imaging system with a complementary RGB sensor. The proposed framework first extracts spatial features from the RGB image using morphological profiles. Then, the fusion model assumes that the extracted features, and the hyperspectral measurements, lie in different subspaces matrices. In addition, this work developed a joint optimization scheme to solve the feature fusion problem by integrating the alternating direction method of multipliers with the block coordinate descent method. The alternating optimizationmethod estimates the spatial features in the fusionmodel by penalizing the l(1) -norm of the spatial gradient magnitudes. The quality of extracted features is measured in terms of supervised pixel-based classificationmethods. Extensive simulations show that the proposed approach outperforms other state-of-the-art methods in terms of classification accuracy.
Keyword:
Hyperspectral imaging system
pixel-based classification
feature fusion
alternating optimization
subspacebased method
l (1) -gradient regularization
synthetic aperture radar.
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