Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
This work addresses semantic segmentation in satellite imagery for automatic feature labeling systems, though it is incremental as it builds on existing methods.
The paper tackled the DSTL Satellite Imagery Feature Detection challenge by adapting a fully convolutional neural network for multispectral data processing, achieving third place out of 419 entries with accuracy comparable to the top solutions.
This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. The primary goal of this challenge is accurate semantic segmentation of different classes in satellite imagery. Our approach is based on an adaptation of fully convolutional neural network for multispectral data processing. In addition, we defined several modifications to the training objective and overall training pipeline, e.g. boundary effect estimation, also we discuss usage of data augmentation strategies and reflectance indices. Our solution scored third place out of 419 entries. Its accuracy is comparable to the first two places, but unlike those solutions, it doesn't rely on complex ensembling techniques and thus can be easily scaled for deployment in production as a part of automatic feature labeling systems for satellite imagery analysis.