目的 高光谱影像（hyperspectral image，HSI）中“同物异谱，异物同谱”的现象普遍存在，使分类结果存在严重的椒盐噪声问题。HSI中的空间地物结构复杂多样，单一尺度的空间特征提取方法无法有效地表达地物类间差异和区分地物边界。有效解决光谱混淆和空间尺度问题是提高分类精度的关键。方法 结合多尺度超像素和奇异谱分析，提出一种新的高光谱影像分类方法，从而充分挖掘地物的局部空间特征和光谱特征，解决空间尺度和光谱混淆的问题，提高分类精度。利用多尺度超像素对影像进行分割，获取不同尺度的分割影像，同时在分割区域内进行均值滤波，减少类内的光谱差异，增强类间的光谱差异；对每个区域计算平均光谱向量，并利用奇异谱分析方法获取光谱的主要鉴别特征，同时消除噪声的影响；利用支持向量机对不同尺度超像素分割影像进行分类，并进行决策融合，得到最终的分类结果。结果 实验选取了两个标准高光谱数据集和一个真实数据集，结果表明，利用本文算法提取的光谱—空间特征进行分类，比直接在原始数据上进行分类分别提高约26.8%、9.2%和13%的精度；与先进的深度学习SSRN （spectral-spatial residual network）算法相比，本文算法在精度上分别提升约5.2%、0.7%和4%，并且运行时间仅为前者的18.3%、45.4%和62.1%，处理效率更高。此外，在训练样本有限的情况下，两个标准数据集的样本分别为1%和0.2%时，本文算法均能取得87%以上的分类精度。结论 针对高光谱影像分类中的难题，提出一种新的融合光谱和多尺度空间特征的HSI分类方法。实验结果表明，本文方法优于对比方法，可以产生更精细的分类结果。

Objective Hyperspectral image (HSI) contains hundreds of spectral bands. The spectral signature of each image pixel acts as a finger print for identification of its material type. Thus, HSI with abundant spectral information is widely used in material recognition and land cover classification. However, the phenomenon of the same objects with dissimilar spectra and the different objects with similar spectra is common in HSI. Specifically, the same ground objects show different spectral curves due to the influence of the surrounding environment, diseases, insect pests, or radioactive substances. Meanwhile, two different features may show the same spectral characteristics in a certain spectral range. As a result, the single spectral information-based classification methods cannot achieve satisfactory ground object discrimination effects. This limitation leads to the salt and pepper noise in the classification maps. Given that HSI is originally 3D cube data, spatial characteristics are complementary to spectral information. The utilization of spatial features, such as shape, texture, and geometrical structures, can improve the land cover discrimination in the pixel-wise classification while reducing the classification noise. However, most spatial methods act on the regular shape or fixed size space area of HSI, which is obviously improper for the complex and diverse land covers. In other words, the region used for spatial feature extraction should adapt to the spatial structure of the image. Moreover, the single-scale feature extraction method in HSI classification cannot effectively express the differences among all land categories and distinguish the boundary of land covers. A multiscale approach appears to be a good solution. We aim to propose an effective classification framework that not only solves the abovementioned problems in HSI but also completes the classification task quickly and efficiently. Method A novel HSI feature extraction and classification method, which combines multiscale superpixel segmentation and singular spectrum analysis (MSP-SSA), is proposed in this study. This method can fuse the local spatial and spectral trend features of land objects. Three main steps are involved in the proposed method, namely, multiscale spatial segmentation, spectral feature extraction, and the use of a classifier-based decision fusion strategy. Superpixel segmentation can divide an image into local homogeneous regions with different sizes and shapes to improve the consistency of spatial structure information. However, a single segmentation scale cannot adequately express the land surface and distinguish the boundary of objects. In detail, too large scales lead to over-segmentation of the region. This condition hinders the full utilization of all samples in the homogeneous region. Meanwhile, too small scales lead to under-segmentation, which makes the samples come from multiple homogeneous regions. Therefore, the multiscale superpixel segmentation is performed in the first step to extract abundant spatial features of complex and various land objects. The first principal component image after principal component analysis is the basic image of segmentation to reduce the subsequent computation. A set of segmentation scales is defined on the basis of the number of benchmark superpixels. The mean filter is also used to further improve the spectral pixel similarity inside superpixels considering the inevitable existence of interference pixels, such as noise, inside the superpixel. Then, the singular spectrum analysis (SSA), named superpixel SSA, acts on each superpixel to extract spectral trend features. After the mean filter, the mean spectral vector is considered to replace the local spatial features of each superpixel. SSA can decompose the mean spectral vector into several sub-components, where each sub-component has the same size as the original vector. Useful information can be enhanced while noise or less representative signals can be effectively suppressed for improving classification accuracy by selecting sub-component(s) to reconstruct the spectral profile. Superpixel SSA acts on the mean spectral vector of each superpixel, and its processing efficiency is mainly affected by the number of superpixels, which significantly reduces the running time compared with the traditional SSA. Finally, a decision fusion strategy based on a classifier is adopted to obtain the final classification results. The support vector machine classifier is utilized to classify the superpixel feature images in different scales due to the robustness of the variation in data dimension. The majority voting decision fusion method is used to obtain the final classification results because of the different scales of the classification results. This method can reduce the probability of pixels being misclassified in a single scale and further improve the classification accuracy. Result We compare our method MSP-SSA with seven state-of-the-art spectral-spatial classification methods that include traditional approaches and deep learning methods on three public datasets, namely, Indian Pines, Pavia University, and 2018 IEEE GRSS Data Fusion Context(IEEE GRSS DFC2018). The quantitative evaluation metrics include overall accuracy (OA), average accuracy (AA), kappa coefficient, and running time. The classification maps of each method are provided for comparison. The classification results show that MSP-SSA outperforms all other methods on three datasets, and the classification maps show that it can effectively eliminate the noise and preserve the object boundary. Compared with the original data, the features extracted by the proposed MSP-SSA can increase by around 21.9%, 8.6%, and 13.5% in terms of OA, nearly 21.6%, 10.3%, and 15.9% in terms of AA, and approximately 0.25, 0.12, and 0.19 in terms of kappa on Indian Pines (5% samples), Pavia University (1% samples), and IEEE GRSS DFC2018 (0.1% samples), respectively. Compared with the metrics of spectral-spatial residual network (SSRN) on three datasets, the OA of the proposed method increases by 2%, 2.3%, and 3.3%, the AA increases by 1.8%, 3.9%, and 7.2%, and the kappa increases by 2%, 2.3%, and 3.3%. The processing efficiency of our method is also higher than that of SSRN, and the running time is only 18.3%, 45.4%, and 62.1% of the latter. Moreover, the proposed method achieves a classification accuracy of over 87% on the Indian Pines and Pavia University datasets with a small number of training samples. Conclusion We propose an effective and efficient HSI classification method by integrating and combining several effective technologies. This method can fuse spectral and multiscale spatial features. Diverse experimental results also demonstrate that MSP-SSA outperforms several state-of-the-art approaches and can produce refined results in classification maps.