# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 5 Discussion  
The obtained results of this study confirm the usefulness of advanced multi-temporal InSAR techniques for detecting and characterizing ground deformations in landslide-prone areas such as western Antalya. The integration of MIRA and InCROSS approaches has  
Figure 9: Obtained results of MIRA (**a**) and InCROSS (**b**). The comparison of the two approaches with RGB color composite image (**c**). enabled a detailed interpretation of deformation dynamics by combining multidimensional visualization, machine learning classification, and statistical transformation. These results are consistent with the previous studies emphasizing the importance of incorporating dimensional reduction and classification techniques to improve remote sensing analysis of landslide activity [43; 50; 52]. The analytical process in this study has significantly benefited from the use of LiCSAR and LiCSBAS, which offer robust solutions for interferometric data processing. LiCSAR facilitates efficient interferogram generation and preprocessing through automated workflows, while LiCSBAS ensures the reliable estimation of deformation velocities based on those outputs. Thanks to their open-source nature, Python-based flexibility, and integration with modules such as atmospheric correction, these tools have been successfully applied in diverse geographic and environmental contexts [54]. Nonetheless, challenges such as reduced data consistency in areas with dense vegetation or low backscatter require that InSAR-based interpretations be supported by ground truth validation when possible. Among the two proposed techniques, MIRA utilizes a multidimensional data space by incorporating InSAR-derived datasets, specifically the east-west (E-W) and vertical (U-D) deformation components. The MIRA approach enables the definition of deformation models in n-dimensional distribution graphs, while also allowing for the integration of all InSAR products into the analysis using a multidimensional data cloud. Although class selection in n-dimensional space may seem subjective in the MIRA approach, this study outlines a roadmap for its selection and determination, explaining how subsequent researchers can be minimally affected by this subjectivity. The most distant members in the data cloud cluster always represent different clusters. The area covered by these clusters corresponds to approximately one-third of this long axis. However, in the MIRA approach, these training classes defined in n-dimensional space are used in the SVM classifier to perform the classification process. The SVM classifier is a non-parametric classifier, and accuracy can be obtained independently of the sample. This strong aspect of the SVM classifier stands out as a factor that increases consistency by disregarding minor differences in selection in n-dimensional space. In brief, the implementation steps of MIRA can be summarized as follows: (1) finding the longest axis of the point cloud in n-dimensional space; (2) marking as different endmembers representation the regions of 1/3 length at both ends of this selected longest axis; (3) assigning these representative clusters as training classes for the SVM. The SVM is a classifier that operates independently of the dimension of the representative cluster, so minor changes to this cluster will not alter its representativeness [45; 46]. These steps reduce or minimize subjectivity due to the recommended standard application procedure. As a result, when combined with the motion information found in the original InSAR datasets, the multidimensional insights provided by MIRA can be very valuable in understanding deformation dynamics. Another approach proposed in this study, InCROSS, is based on principal component analysis (PCA) and the Crosta technique, and relies on a more straightforward application principle and statistical calculations. This method benefits from prior knowledge of landslide locations and enhances structural patterns within the data, allowing for more consistent identification of the deformation zones.