  Remote sensing (RS) images are important to monitor and survey earth at
varying spatial scales. Continuous observations from various RS sources
complement single observations to improve applications. Fusion into single or
multiple images provides more informative, accurate, complete, and coherent
data. Studies intensively investigated spatial-temporal fusion for specific
applications like pan-sharpening and spatial-temporal fusion for time-series
analysis. Fusion methods can process different images, modalities, and tasks
and are expected to be robust and adaptive to various types of images (e.g.,
spectral images, classification maps, and elevation maps) and scene
complexities. This work presents solutions to improve existing fusion methods
that process gridded data and consider their type-specific uncertainties. The
contributions include: 1) A spatial-temporal filter that addresses spectral
heterogeneity of multitemporal images. 2) 3D iterative spatiotemporal filter
that enhances spatiotemporal inconsistencies of classification maps. 3)
Adaptive semantic-guided fusion that enhances the accuracy of DSMs and compares
them with traditional fusion approaches to show the significance of adaptive
methods. 4) A comprehensive analysis of DL stereo matching methods against
traditional Census-SGM to obtain detailed knowledge on the accuracy of the DSMs
at the stereo matching level. We analyze the overall performance, robustness,
and generalization capability, which helps identify the limitations of current
DSM generation methods. 5) Based on previous analysis, we develop a novel
finetuning strategy to enhance transferability of DL stereo matching methods,
hence, the accuracy of DSMs. Our work shows the importance of spatial,
temporal, and geometric fusion in enhancing RS applications. It shows that the
fusion problem is case-specific and depends on the image type, scene content,
and application.
