LAUSR

Deep learning methods have gained rapid development in hyperspectral pansharpening (HP) due to powerful spatial–spectral feature extraction ability. However, most of these methods are optimized using a single reconstruction objective. It is difficult for these methods to find a balance between spectral preservation and spatial preservation. Furthermore, these methods adopt interpolation or convolution to upsample the hyperspectral images (HSIs), which tends to cause noticeable spectral distortion. To conquer these issues, a novel multiobjective guided divide-and-conquer network (MO-DCN) is proposed for HP. It consists of a deconvolution long short-term memories (LSTMs) network (DLSTM) and a divide-and-conquer network (DCN). DLSTM leverages bi-direction learning to upsample HSIs by considering 3-D spatiotemporal dependencies. Then, DCN designs a two-branch architecture to reconstruct spatial and spectral information from upsampled HSIs and panchromatic images (PANIs), respectively, where the spatial branch designs an attention-in-attention module (AIAM) to emphasize complementary attention in a coarse-to-fine way. Finally, co-improvement of spatial and spectral information is formulated as an Epsilon-constraint-based multiobjective optimization. The Epsilon constraint method transforms one objective into a constraint and regards it as a penalty bound to make an excellent tradeoff between different objectives. Experimental results demonstrated that the proposed method markedly improves pansharpening performance in both the spatial and spectral domains and has superior fusion performance than state-of-the-art methods.