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.

本文讲述了我们在 Kaggle 组织的 DSTL 卫星图像特征检测挑战中的方法，基于卷积神经网络（FCN）改进实现多谱段数据处理，通过修改目标函数、全局训练流程和应用数据增强策略以及反射率指数等，最终在 419 个团队中获得了第三名，精度可与前两名媲美。与其它获胜方案不同的是，我们的解决方案不依赖于复杂的集成技术，因此可以轻松扩展用于生产中的自动特征标注系统的卫星图像分析中。

使用深度卷积神经网络进行卫星图像特征检测: Kaggle竞赛