Abstract
Most spaceborne sensors have an inevitable tradeoff between spatial and spectral resolutions. This is a typical ill-posed inverse problem in the field of image fusion. To solve this problem, this letter proposes an image fusion method using spatial principal component analysis and modulation transfer function-based filters. The key behind the proposed fusion method is to efficiently estimate the missing spatial details by considering the spatial structures of the low-resolution multispectral (MS) imagery. Also, this letter proposes a newly developed injection gain model to resolve the local and global dissimilarity between panchromatic and MS imageries, which could prevent over- and under-injections. Finally, spatial details, optimized to be injected into the MS images, were constructed and paired with the developed injection gain model to produce high-resolution MS images. Two data sets acquired by WorldView-2 are employed for validation. The experimental results demonstrate that the proposed fusion method generates high-quality imagery in terms of both qualitative and quantitative standards.
Minho Kim
PhD Candidate
Data-driven, machine learning-based environmental modeling using geospatial data.