Identifying Poultry Farms from Satellite Images with Residual Dense U-Net

Abstract

In this paper, we proposed a convolutional neural network called residual dense U-Net. This network is devised based on the original U-Net network. The encoder-decoder architecture in U-Net can restore the feature map to the resolution of the original image and obtain high-level semantic features. The skip-connection in U-Net can fuse the features after up-sampling and down-sampling to prevent both high-level semantic features and low-level semantic features from being lost after down-sampling. In the encoder and decoder parts, we utilize the residual dense block (RDB) from Residual Dense Network. Before each max-pooling, we replace the last convolutional layer in the original U-Net architecture with RDB. After each up-sampling, the last convolutional layer in the original U-Net architecture will also be replaced with RDB. The proposed method will be used to find poultry farms in Taiwan from satellite images. The prediction results will be evaluated using several indicators such as IOU, precision, recall, and F1-score.