Adaptive regularized scheme for remote sensing image fusion

We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a ＂grey world＂ assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation （TV）-LI term for edges and L2-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler-Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.

Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion for remote sensing image

Remote sensing image(RSI)with concurrently high spatial,temporal,and spectral resolutions cannot be produced by a single sensor.Multisource RSI fusion is a convenient technique to realize high spatial resolution multispectral(MS)images(spatial spectral fusion,i.e.SSF)and high temporal and spatial resolution MS images(spatiotemporal fusion,i.e.STF).Currently,deep learning-based fusion models can only implement SSF or STF,lacking models that perform both SSF and STF.Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion(BAPGF)for RSI are proposed to implement both SSF and STF,namely BPF-MGAN.A bidirectional adaptive-stage feature extraction architecture infine-scale-to-coarse-scale and coarse-scale-to-fine-scale modes is introduced.The designed BAPGF introduces a previous fusion result-oriented cross-stage-level dual-residual attention fusion strategy to enhance critical information and suppress superfluous information.Adaptive resolution U-shaped discriminators are implemented to feed multiresolution context into the generator.A generalized multitask loss function unlimited by no-reference images is developed to strengthen the model via constraints on the multiscale feature,structural,and content similarities.The BPF-MGAN model is validated on SSF datasets and STF datasets.Compared with the state-of-the-art SSF and STF models,results demonstrate the superior performance of the proposed BPF-MGAN model in both subjective and objective evaluations.

Spatio-temporal-spectral observation model for urban remote sensing

Taking cities as objects being observed,urban remote sensing is an important branch of remote sensing.Given the complexity of the urban scenes,urban remote sensing observation requires data with a high temporal resolution,high spatial resolution,and high spectral resolution.To the best of our knowledge,however,no satellite owns all the above character-istics.Thus,it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation.In this study,we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model,filling the gap of no existing urban remote sensing framework.In this study,we present four applications to elaborate on the specific applications of the proposed model:1)a spatiotemporal fusion model for synthesizing ideal data,2)a spatio-spectral observation model for urban vegetation biomass estimation,3)a temporal-spectral observation model for urban flood mapping,and 4)a spatio-temporal-spectral model for impervious surface extraction.We believe that the proposed model,although in a conceptual stage,can largely benefit urban observation by providing a new data fusion paradigm.