Boosting object detection performance through ensembling on satellite imagery
How image analysts and object detection algorithms can benefit from the latest advances in ensembling techniques.
In Machine Learning (ML) projects, the last percentages of performance are the most difficult and time-consuming to reach.
The reason behind that is that they are related to the hardest cases.
If we take the case of civil vehicle detection on VHR (Very High Resolution) satellite images, some hard cases could, for instance, be objects that look similar in both desert environments (small trees, rocks and buildings) and in working areas (containers). It can also be bad quality images, partly hidden vehicles (by buildings, clouds or vegetation), vehicles in the shadow or even in the snow.
At Earthcube, many of our clients rely on satellite imagery analysis and need very high-performance algorithms. So the last percentages of performance are also the most important to achieve.
Ensembling as an alternative at Earthcube
To convert a good performance model into a very high-performance model, even an experienced ML team has only two options that are time-consuming: 1) increase the size of the training set by getting more labelled data
2) perform lots of tests to optimize the model architecture and hyper-parameters.
Thanks to the latest advances in the scientific community and our last developments, ensembling has now become, at Earthcube, a new alternative that is less time-consuming yet still very efficient for satellite imagery. It is currently used for civil vehicle detection and other observables.
What is ensembling?
In our case, ensembling is a technique that aims to maximize the final detection performance by fusing individual detectors.
While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high scores in recent data science competitions, such as Kaggle. The few remote sensing articles mentioning ensembling mainly focus on mid-resolution images…