Applying computer vision and machine learning for land cover mapping.
Applying computer vision to overhead imagery has the potential to detect emerging natural disasters, improve response, quantify the direct and indirect impact — and save lives. A VLL team is working on this challenge.
We present the first study of constructing adversarial examples for non-RGB imagery, and show that non- RGB machine learning models are vulnerable to adversarial. We propose a framework to make non-RGB image-based semantic segmentation systems robust to adversarial attacks. examples.
We propose Integrated Learning and Feature Selection (ILFS) as a generic framework for supervised dimensionality reduction. We demonstrate ILFS is effective for dimensionality reduction of multispectral and hyperspectral imagery, and significantly improves performance on the semantic segmentation task for high dimensional imagery.
We work on finding spatial feature correspondence between images generated by sensors operating in different regions of the spectrum, in particular the Visible (Vis: 0.4-0.7 um) and Shortwave Infrared (SWIR: 1.0-2.5 um). Under the assumption that only one of the available datasets is geospatial ortho-rectified (e.g., Vis), this spatial correspondence can play a major role in enabling a machine to automatically register SWIR and Vis images, representing the same swath, as the first step toward achieving a full geospatial ortho-rectification of, in this case, the SWIR dataset.