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Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks
The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for the landcover classification, especially concerning the vegetation assessment.
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation
To overcome these data restrictions, it is common to consider various approaches including data augmentation techniques.
Targeted change detection in remote sensing images
Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection).
Satellite imagery analysis for operational damage assessment in Emergency situations
When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams.
