Journal
REMOTE SENSING
Volume 13, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/rs13214441
Keywords
remote sensing images; building extraction; transformers; sparse token sampler
Categories
Funding
- National Key Research and Development Program of China [2019YFC15 10905]
- National Natural Science Foundation of China [62125102]
- Beijing Natural Science Foundation [4192034]
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This paper explores the potential of using transformers for efficient building extraction and designs an efficient dual-pathway transformer structure that achieves state-of-the-art accuracy on benchmark datasets.
Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of sparse feature vectors in their feature space by introducing a new module called sparse token sampler . With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.
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