# Unsupervised Feature Learning in Remote Sensing

## TL;DR

This paper demonstrates the application of a state-of-the-art unsupervised learning algorithm to remote sensing data, enabling effective feature extraction for various tasks without requiring labeled data.

## Contribution

It introduces an unsupervised feature learning approach tailored for remote sensing data, capable of handling noisy, imbalanced datasets and multiple tasks.

## Key findings

- Effective visual similarity search across classes
- Successful outlier detection in imbalanced data
- Automatic learning of class hierarchies

## Abstract

The need for labeled data is among the most common and well-known practical obstacles to deploying deep learning algorithms to solve real-world problems. The current generation of learning algorithms requires a large volume of data labeled according to a static and pre-defined schema. Conversely, humans can quickly learn generalizations based on large quantities of unlabeled data, and turn these generalizations into classifications using spontaneous labels, often including labels not seen before. We apply a state-of-the-art unsupervised learning algorithm to the noisy and extremely imbalanced xView data set to train a feature extractor that adapts to several tasks: visual similarity search that performs well on both common and rare classes; identifying outliers within a labeled data set; and learning a natural class hierarchy automatically.