Feature Pyramid Network for Multi-Class Land Segmentation
This work addresses land cover classification for applications like urban planning and environmental monitoring, but it is incremental as it applies an existing FPN method to a specific dataset.
The paper tackles multi-class land segmentation in satellite imagery using a feature pyramid network with a ResNet50 encoder, achieving reliable results on the DEEPGLOBE challenge and enabling efficient training on GTX 1080/1080 TI cards.
Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.