Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. In this paper, to better

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... act the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (namely, Rolling Guidance Sparse Unmixing (RGSU)) is proposed to extract and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery have different meanings and importance. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The proposed method is based on rolling guidance spatial regularization in a traditional spatial regularization sparse unmixing framework, and it accomplishes scale-aware sparse unmixing. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves visual effects better and produces higher quantitative results (i.e., higher SRE values) when compared to the current state-of-the-art sparse unmixing algorithms, which illustrates the effectiveness of the rolling guidance based scale aware method. In the future work, adaptive scale-aware spatial sparse unmixing framework will be studied and developed to enhance the current idea. the presence of mixed pixels [3] [4] [5] . Spectral unmixing is a common way to solve this mixed pixel problem, and it is aimed at estimating the fractional abundances of the pure spectral signatures or endmembers in each mixed pixel with linear or nonlinear mixture models [6, 7] . The linear mixture model expresses the measured spectral signature as a linear combination of several distinct typical materials or endmembers, while the nonlinear mixture model assumes that the incident radiation interacts with more than material, and it is affected by multiple scattering effects [8] . When compared with the nonlinear mixture model, the linear mixture model has been extensively studied as a result of its computational tractability and its flexibility in different applications, and the fact that it also holds in macroscopic remote sensing scenarios. Therefore, in this paper, we focus on linear spectral unmixing analysis. The traditional spectral unmixing approaches consist of three basic ideas to precisely estimate the endmember signatures and the corresponding fractional abundances. One idea can be called the supervised methods, which compute the abundances or endmember signatures based on the known precise endmember signatures [9,10] or abundances [11, 12] . The second idea is the unsupervised methods, which are sometimes referred to as Blind Source Separation (BSS) [13] [14] [15] [16] [17] , which assume that the spectral components are statistically independent. The last idea is the semi-supervised methods, which express the mixed pixels using a large standard spectral library known in advance, and estimate the fractional abundances, as well as activating the corresponding materials' standard spectral signatures [18] [19] [20] [21] . Approaches of the first series have been studied for many years, and include the Pixel Purity Index (PPI) [22] , N-FINDR [23], Fully Constrained Least Squares (FCLS) [9], and Abundance-Constrained Endmember Extraction (ACEE) [11]. Independent Component Analysis (ICA) [24], Non-negative Matrix Factorization (NMF) [25], and Sparse Component Analysis (SCA) [26] belong to the second spectral unmixing idea. In recent years, the semi-supervised unmixing methods have attracted lots of attention as they make full use of a standard spectral library, and they also effectively circumvent the challenging endmember identification step [2] , which is replaced with activating the corresponding endmember signatures over the large standard spectral library, which is given as prior knowledge. Sparse unmixing, as one of the typical semi-supervised spectral unmixing methods, reformulates the linear spectral unmixing problem as selecting endmembers from a standard spectral library using sparse regression [8] . Since the research into sparse unmixing has progressed, a number of sparse unmixing algorithms have been proposed, such as Sparse Unmixing via variable Splitting and Augmented Lagrangian (SUnSAL) [27], Sparse Unmixing via variable Splitting and Augmented Lagrangian and Total Variation (SUnSAL-TV) [28], Non-Local Sparse Unmixing (NLSU) [29] , and Collaborative SUnSAL (CSUnSAL) [19] , and its variants [30, 31] . Spatial sparse unmixing incorporates the spatial information into sparse unmixing and utilizes the existing spatial correlations, leading to a higher unmixing accuracy and a better visual effect [32] . Hence, the spatial sparse unmixing methods should be a worthwhile approach for hyperspectral remote sensing image processing. The current spatial-spectral unmixing methods usually use the spatial information between each pixel and its near neighbors, e.g., the Total Variation (TV)-based regularization model [33] and the mathematical morphological methods [34], or region-based spatial consideration, e.g., the sliding-window based approaches [35] . Most of these approaches aim to preserve the edges and remove detrimental or unwanted content. However, with the spatial regularization based methods, only high-contrast edges or textures can be extracted, and low-contrast or gradual changes in the original remote sensing images are ignored, which results in the loss of small structures, which are usually referred to as "details". In this paper, a new spatial sparse unmixing algorithm based on rolling guidance as a scale-aware operation, namely, Rolling Guidance Sparse Unmixing (RGSU), is proposed. In RGSU, the rolling guidance scale-aware model [36] is designed as the spatial regularization by considering and controlling the different level of details with iterations following scale-space theory. The rolling guidance idea has been used in image denoising, detail enhancement, edge extraction, image