Toward the Vectorization of Hyperspectral Imagery.

Hyperspectral images (HSIs) can provide rich spectral–spatial information that has been widely utilized in many fields, such as national defense, mineralogy, and agriculture. Most of the recent HSI interpretation methods are conducted in the raster pattern, which results in high memory costs, amplification distortion, and difficulties in topological editing. To address this issue, a novel end-to-end vectorization framework is proposed, called as the HSI vectorization network (HSI-VecNet), which learns a vector representation from spectral–spatial information through cross-level interactions. Specifically, this framework integrates low-level geometry information and high-level semantic instance information, which consists of two branches: the HSI semantic instance segmentation (HSIS) and the spectral-spatial junction prediction (SSJP). The HSIS conducts the raster-based classification and extracts the semantic information of each object in the HSI. In addition, the SSJP exploits spectral–spatial information to predict the positions of junctions in the HSI. The instance information of each object and the relations of junctions are then fused to vectorize the HSI. To verify the effectiveness of the proposed method, four hyperspectral datasets are vectorially labeled. Experimental results on these datasets demonstrate that the proposed end-to-end HSI-VecNet outperforms existing post-process vectorization methods. Our model and datasets will be made publicly available at https://github.com/yyyyll0ss/HSI-VecNet.