Deep Aggregation Net for Land Cover Classification

Tzu-Sheng Kuo, Keng-Sen Tseng, Jia-Wei Yan, Yen-Cheng Liu, Yu-Chiang Frank Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 252-256

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.

Related Material


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[bibtex]
@InProceedings{Kuo_2018_CVPR_Workshops,
author = {Kuo, Tzu-Sheng and Tseng, Keng-Sen and Yan, Jia-Wei and Liu, Yen-Cheng and Frank Wang, Yu-Chiang},
title = {Deep Aggregation Net for Land Cover Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}