# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 1 Introduction  
Hyperspectral image (HSI) classification is one of the most active and attractive topics in the field of remote sensing. Compared with multispectral sensors, hyperspectral sensors can capture more abundant information of land cover, such as agricultural crop types. This detailed spectral-spatial information will greatly increase the discriminative ability of HSI. In recent years, HSI classification has been successfully applied to precision agriculture [1; 2], environment monitoring [3; 4], ocean exploration [5], object detection [6; 7], and so on. However, it continues to be a challenging problem to develop new effective spectral-spatial methods to classify HSI accurately, due to its high dimension, large data volume, massive noisy pixels, and few training samples. HSI classification needs to assign a meaningful class label to each pixel within the image according to the spectral feature or spectral-spatial information. A large number of effective HSI classification techniques have been developed in the past decades. Early HSI classification methods mainly used the spectral features to classify hyperspectral data [8; 9; 10; 11; 12]. Although the classification results obtained by these methods may not be good enough, this is a successful attempt to apply the machine learning method to hyperspectral remote sensing. Lately, spatial structure information has been gradually taken into account in some pixel-based classification approaches [13; 14; 15; 16; 17; 18; 19; 20], aiming at getting better classification results. Generally speaking, the purpose of introducing spatial information into the process of classification can be roughly understood as denoising HSI in preprocessing [21; 22; 23; 24], defining the novel similarity between a pair of pixels [25; 26], reducing dimensionality [27], improving the classification map in post-processing [28; 29; 30], or their combinations. From the point of view of classification accuracy, the existing literatures show that some spectral-spatial HSI classification methods do have good classification performance. In recent times, more attention has been paid to spectral-spatial HSI classification approaches based on superpixel in remote sensing. Unlike the fixed-size window commonly used in the Markov random field technique [31; 32], a superpixel is a homogeneous region with adaptive shape and size. Specifically, superpixel-based classification methods first segment HSI into superpixels, and then integrate various techniques and classifiers to process hyperspectral data [33; 34; 35; 36]. For example, the SuperPCA method developed an intrinsic low dimensional feature of HSI by using principal component analysis (PCA) on each superpixel [35]. [PERSON] et al. presented an effective classification method by using the joint sparse representation on different superpixels [36]. On the basis of multiscale superpixels and guided filter, [PERSON] and [PERSON] depicted a spectral-spatial HSI classification method [24]. In addition, the homogeneity of superpixels together with the majority voting strategy were also adopted to improve the classification map in some spectral-spatial HSI classification methods [37; 38; 39]. These superpixel-based HSI classification methods at pixel level utilize the spatial information of HSI detected by the superpixels to improve the performance of classification algorithms. Very recently, under the assumption that pixels within a superpixel should share the same class label, two superpixel-level HSI classification methods have been proposed. Based on an affine hull model and set-to-set distance, [PERSON] et al. developed a spectral-spatial HSI classification method at superpixel level [40]. [PERSON] et al. proposed a graph-based semi-supervised classification technique for HSI by defining the weight between two adjacent superpixels [41]. The classification at superpixel level is, to some degree, a reduction of pixels, which may greatly reduce the number of objects in the classification process.