A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.
The isomap algorithm and topological stability,
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A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.
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Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.
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Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization
A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.