融合空间信息的SLIC聚类高光谱分解方法

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"基于区域聚类的高光谱分解空间预处理是一种旨在改善遥感图像高光谱分解的技术。高光谱分解的核心是将混合像素分解为纯净的光谱成分，即末端成员，以及它们相应的分数丰度。然而，传统的光谱方法未能充分利用空间信息，忽略了像素间的空间相关性。为了克服这一局限，本文提出了一种新的空间预处理方法，它融合了SLIC（简单线性迭代聚类）算法，以实现对局部区域的空间信息的高效利用。这种方法将聚类与空间和光谱信息相结合，保持了计算复杂度的线性增长，提高了处理效率。首先，该方法通过迭代执行区域聚类，结合空间和光谱特性，形成一组体现光谱相似性和空间相关性的聚类分区。接着，针对每个分区，选取具有高光谱纯度的像素子集作为候选。最后，这些候选像素被聚合起来，并输入到基于光谱的端成员提取算法中，以获取最终的端成员和分数丰度。这种方法的创新之处在于它自然地整合了空间和光谱信息，确保了在分解过程中保留最重要的特征。关键词：高光谱分解、空间和光谱信息集成、聚类、SLIC（简单线性迭代聚类）、端成员提取算法。本文的贡献在于提供了一种有效的方法，以改进高光谱图像的分析效果，特别是在复杂环境下的应用，如环境监测、资源探测和目标识别等。通过将空间信息更好地融入解混过程，可以提高分解的准确性和可靠性，为遥感图像处理提供了新的思路和技术支持。"

combined with a spatial distance weight, is adopted to determine the

purity of each candidate endmember in the neighborhood window. In

the SSPP method (Martín and Plaza, 2012), the pixel's spectral purity is

characterized by using the Root Mean Square Error (RMSE) (Keshava

and Mustard, 2002) between two images: the original image and a

ﬁltered version obtained by using Multi-Scale Gaussian Filtering

(Young and Van Vliet, 1995). The lower the RMSE, the higher the si-

milarity between a pixel and its neighbors. In Xu et al. (2015),a

Homogeneous Index (HI) is assigned to each candidate. Such HI is

calculated by using the SID criterion between itself and its neighbors,

and the maximum SID value is adopted. In the SSPM method (Kowkabi

et al., 2016b), the values in cluster labels between the candidate and its

neighbors are adopted as the homogeneity criterion, where the cluster

label for each pixel is obtained by the K-means clustering algorithm.

The basic idea of all the above mentioned methods is to perform

spectral similarity measurements within a local neighborhood window

by using a spectral distance metric, such as the Euclidean Distance (ED),

Spectral Correlation Measure (SCM), SAD, and SID. However, there are

some pending issues in those methods. For instance, in the AMEE

method (Plaza et al., 2002), the properties of the spatial kernel (shape

and size) strongly inﬂuence the MEI result. Although a progressively

increased kernel size can be adopted, this will heavily increase the

computational burden. The problem of determining an optimal neigh-

borhood size also exists in other methods (Li and Zhang, 2011; Martín

and Plaza, 2012; Xu et al., 2015; Kowkabi et al., 2016b). In addition,

the setting of parameters is a diﬃcult issue for all methods.

For better examining the spatially homogeneous regions in hyper-

spectral images, unsupervised clustering methods have been adopted in

existing spatial-spectral unmixing models. Speciﬁcally, RBSPP (Martín

and Plaza, 2011), SSPP (Martín and Plaza, 2012) and SSPM (Kowkabi

et al., 2016b) apply unsupervised clustering algorithms [such as

ISODATA (Ball and Hall, 1965), K-means, or the Hierarchical Seg-

mentation (HSEG) algorithm (Tilton, 2003)] to segment the original or

transformed image [e.g., by using the Principal Component Transform

(PCT)] into a set of spectral clusters, each made up of one or more

spatially connected regions. Compared with the SSEE (Rogge et al.,

2007a), SPEE (Mei et al., 2010), HEEA (Li and Zhang, 2011), LLC

(Canham et al., 2011) and the method in Xu et al. (2015), which all

segment the original image into non-overlapping sub-blocks with a

ﬁxed size and shape, the partitions obtained by clustering algorithms

generally exhibit better smoothness and coherence. However, in most

of the aforementioned clustering algorithms, only spectral information

is considered during the clustering process, and the size of segmented

partitions is subject to diversity and inconsistencies. The SSPP method

(Martín and Plaza, 2012), which includes the spatial information,

however fuses the spatial and spectral information in a separate way.

Therefore, how to naturally combine the spatial information during the

clustering stage is a main diﬃculty for SSPP. In addition, other relevant

issues such as the computational complexity of clustering algorithms,

the determination of the number of clusters in advance, and the com-

putational e

ﬃciency

need further discussion.

In recent years, many superpixel-based segmentation methods, such

as Graph-based Algorithms (Felzenszwalb and Huttenlocher, 2004),

Turbopixel method (Levinshtein et al., 2009), and SLIC (Achanta et al.,

2012), have been proposed. In essence, any image segmentation

method can generate superpixels, and the aforementioned methods

show faster speed and more accurate segmentation results than some

traditional methods such as Mean-Shift-based (Vedaldi and Soatto,

2008) and Watershed-based approaches (Vincent and Soille, 1991).

Superpixel methods have also been widely used in the hyperspectral

imaging community; relevant work can be found in Thompson et al.

(2010) and Saranathan and Parente (2016). In hyperspectral images, a

superpixel represents a homogeneous region which contains a number

of pixels that exhibit spatial continuity and spectral similarity. When

superpixels are used to represent spatial correlation information in

hyperspectral images, compared with the ﬁxed size and shape of

window-based methods, they can represent adaptive spatial neighbor-

hoods as expected in natural scenes, which generally exhibit arbitrary

morphologies or sharp boundaries, thus reducing the sensitivity to

noise and outliers and signiﬁcantly improving the computational eﬃ-

ciency for subsequent processing tasks, such as endmember extraction

and spectral unmixing. In Thompson et al. (2010) and Saranathan and

Parente (2016),aneﬃcient graph-based image segmentation algorithm

(Felzenszwalb and Huttenlocher, 2004), which performs an agglom-

erative clustering of pixels as nodes on a graph such that each super-

pixel represents the minimum spanning tree of the constituent pixels, is

adopted. This method adjusts well to natural image boundaries, but

produces superpixels with very irregular sizes and shapes (Achanta

et al., 2012). In Massoudifar et al. (2014), the well known Ultrametric

Contour Map (UCM) algorithm is extended to hyperspectral images,

conducting comparative experiments on the original hyperspectral

image, the transformed image by PCT, and a monochromatic image,

respectively. The results indicate that superpixel estimation on the

transformed image is generally more eﬃcient than other methods. The

most important aspect of superpixel segmentation methods is how to

determine the number of superpixels. In order to obtain a good super-

pixel representation, over-segmentation is generally adopted. In

Saranathan and Parente (2016), a segment uniformity criterion is pro-

posed to control the segmentation scale, which adopts a threshold to

limit maximum variability inside one segment. The threshold is com-

puted by a statistical analysis of the within-class and between-class

spectral divergences of several endmember classes.

In Saranathan and Parente (2016), Thompson et al. (2010), and

Massoudifar et al. (2014), superpixel segmentation methods have been

shown to succeed when applied to hyperspectral imagery, and pre-

sented competitive results and signiﬁcant improvements in the sub-

sequent processing steps (particularly from a computational stand-

point). However, the processing time of these superpixel segmentation

methods increases as the size of the images become larger. As a result,

ﬁner segmentations are likely to result in signiﬁcant computation times

and sensitivity to noise. Based on this observation, in this work we have

developed a new spatial preprocessing approach for hyperspectral un-

mixing. Compared with the aforementioned superpixel segmentation

methods, the proposed approach is faster and requires less memory

space. In addition, its computational complexity is not aﬀected by the

image size. A detailed description of our proposed method is given in

the following section.

3. Regional clustering-based spatial preprocessing

In this section we describe a new method for spatial preprocessing

which consists of two main steps. The

ﬁrst one is a clustering procedure

which divides the original image into a set of homogeneous partitions.

As opposed to the RBSPP (Martín and Plaza, 2011) and the SSPP

(Martín and Plaza, 2012), we introduce a new eﬃcient clustering

strategy which naturally integrates the spatial and the spectral in-

formation contained in the data. In a second step, we extract candidate

pixels from each partition. After these two steps, the candidate pixels

are gathered together and fed to a spectral-based method to obtain the

ﬁnal endmembers and their corresponding abundances. An illustrative

ﬂowchart of the method is shown in Fig. 1. A detailed description of

each step is given below.

3.1. Regional clustering

The proposed clustering strategy is inspired by the SLIC algorithm.

The core idea of SLIC is to constrain the search scope from the whole

image to a local region around the centroid. Compared with the SLIC

X. Xu et al.

Remote Sensing of Environment 204 (2018) 333–346

335

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