Deep Aggregation Net for Land Cover Classification

Tzu-Sheng Kuo, Keng-Sen Tseng, Jia-Wei Yan, Yen-Cheng Liu, Yu-Chiang Frank Wang

Abstract: Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. In this paper, we propose a deep aggregation network for solving this task, which extracts and combines multi-layer features during the segmentation process. In particular, we introduce soft semantic labels and graph-based fine tuning in our proposed network for improving the segmentation performance. In our experiments, we demonstrate that our network performs favorably against state-of-the-art models on the dataset of DeepGlobe Satellite Challenge, while our ablation study further verifies the effectiveness of our proposed network architecture.