The emerging advances in imaging technologies pave the way for the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) in Earth sciences. Recently, hyperspectral imaging techniques have arisen as the most important tool to remotely acquire finespectral information from different materials/organisms. Nonetheless, such datasets require dedicated processing for most applications due to the 1) highdimensionality of an HSI and 2) highlymixed nature of pixels within an HSI. In addition, finespectral information usually comes at the cost of coarse spatial resolution due to the tradeoff between spectral and spatial resolutions in hyperspectral imaging systems. Therefore, several machine learning techniques (e.g., supervised learning and unsupervised learning) were proposed in the last decades to alleviate such challenges.
Unsupervised learning techniques have become popular among the proposed machine learning techniques since they do not rely on labeled samples for clustering. Data points in a highdimensional dataset can be drawn from a union of lowerdimensional subspaces, thus subspacebased clustering approaches, specifically, sparse subspace clustering (SSC) concept has drawn special attention to cluster highdimensional data into meaningful groups. SSCbased approaches benefit from the socalled "selfexpressiveness" property, where each data point can be written as a linear combination of other data points from the same subspace. Such algorithms, hence are able to process and tackle highdimensional and highlymixed nature of HSIs, as is the case in realworld applications (e.g., urban, landcover, and mineralmapping). However, the superior performance of SSC is counterbalanced with demanding high computational power and being timeconsuming compared to traditional clustering approaches. In addition, the number of clusters of interest needs to be predefined prior to the clustering procedure.
We proposed the following studies to mitigate the aforementioned challenges and develop automatic, robust, and fast clustering approaches to analyze remote sensing datasets.

We studied the performance of different sparse subspacebased clustering algorithms on drillcore hyperspectral domaining [1];

We developed a fast, robust, and automatic sparse subspacebased clustering algorithm, the "hierarchical sparse subspace clustering (HESSC)" to analyze HSIs " [2];

To incorporate spatial information in the clustering procedure, we proposed a hiddenMarkov random subspacebased clustering algorithm for HSI analysis [3];

To improve the final clustering result and fully exploit spatial information in the clustering procedure, we proposed a multisensor hiddenMarkov random subspacebased clustering and multisensor sparsebased clustering (MultiSSC) algorithms, where the former utilizes a postprocessing step to refine the generated in accordance with spatial information, while the latter uses the spatial and contextual information within the clustering structure schema. Worthy to indicate that the spatial and contextual information is derived from high spatialresolution images, whereas the rich spectral information is extracted from an HSI [4], [5].

Prior to any analysis procedure, one needs to conduct preprocessing steps to decrease the effect of the noise (e.g., atmospheric effects, instrumental noises) contaminating the data. It is crucial to precisely carry on the preprocessing steps. We studied the impact of applying a denoising technique before and after atmospheric corrections. The observations challenge the current de facto paradigm of denoising in a processing chain of spaceborne and airborne remotely sensed images [6].
References
[1] Shahi, K. R., Khodadadzadeh, M., TolosanaDelgado, R., Tusa, L., and Gloaguen, R. (2019, September). The Application Of Subspace Clustering Algorithms In DrillCore Hyperspectral Domaining. In 2019 10\textit{th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)} (pp. 15). IEEE.
[2] Rafiezadeh Shahi, K., Khodadadzadeh, M., Tusa, L., Ghamisi, P., TolosanaDelgado, R., and Gloaguen, R. (2020). Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis. \textit{Remote Sensing}, 12(15), 2421.
[3] Rafiezadeh Shahi, K., Ghamisi, P., Jackisch, R., Khodadadzadeh, M., Lorenz, S., and Gloaguen, R. (2020). A New SpectralSpatial Subspace Clustering Algorithm For Hyperspectral Image Analysis. \textit{ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences}. V32020. 185191. 10.5194/isprsannalsV320201852020.
[4] Rafiezadeh Shahi, K., Ghamisi, P., Jackisch, R., Rasti, B., Scheunders, P., and Gloaguen, R. A multisensor subspacebased clustering algorithm using RGB and hyperspectral data. In 2021 11\textit{th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)}(pp. 15). IEEE.
[5] Rafiezadeh Shahi, K., Ghamisi, P., Rasti, B., Jackisch, R., Scheunders, P., and Gloaguen, R. (2020). Data Fusion Using a MultiSensor SparseBased Clustering Algorithm. Remote Sensing, 12(23), 4007.
[6] Rafiezadeh Shahi, K., Rasti, B., Ghamisi, P., Scheunders, P., and Gloaguen, R. When is the right time to apply denoising. In 2021 IEEE Geoscience and Remote Sensing Symposium, IEEE.