LAUSR

Due to the complex environment of hyperspectral image (HSI) gathering area, it is difficult to obtain a large number of labeled samples for HSI. Therefore, how to effectively achieve the HSI few-shot classification is a hot spot of current research. Prototypical network (PN) is one of the most classical few-shot learning algorithms, which has been widely employed for few-shot image classification and few-shot object detection. However, existing PN-based algorithms for HSI only utilize the single-scale spatial–spectral feature extracted from the last layer, ignoring the semantic information with different scales contained in the other layers. To solve this problem, a novel multiscale spatial–spectral PN (MSSPN) is proposed in this letter. The contribution of this letter is threefold. First, a multiscale spatial–spectral feature extraction algorithm based on ladder structure is proposed to effectively achieve the integration of spatial–spectral features with different scales. Second, with the theory of ladder-structure-based extraction algorithm, we design a multiscale spatial–spectral prototype representation, which is suggested to be more robust and effective in the multiscale spatial–spectral metric space. Finally, our proposed MSSPN has the advantage of expandability, and can be easily applied for the other PN-based few-shot learning methods. The experimental results on HSI few-shot classification indicate that our proposed MSSPN algorithm can achieve higher accuracy than the representative HSI classifiers and the existing PN-based algorithms.