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Found 5 papers, 1 papers with code
Class-Wise Principal Component Analysis for hyperspectral image feature extraction
This paper introduces the Class-wise Principal Component Analysis, a supervised feature extraction method for hyperspectral data.
Procedural 3D Terrain Generation using Generative Adversarial Networks
Combining the generated DEM and RGB image, we are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training.
Land Cover Semantic Segmentation Using ResUNet
For this purpose convolutional machine learning models were trained in the task of predicting the land cover semantic segmentation of satellite images.
Neural Style Transfer for Remote Sensing
The well-known technique outlined in the paper of Leon A. Gatys et al., A Neural Algorithm of Artistic Style, has become a trending topic both in academic literature and industrial applications.
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions
In this work, very deep super-resolution (VDSR) method is presented for improving the spatial resolution of remotely sensed (RS) images for scale factor 4.
