Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approach can explain the scattering process of certain type of targets by use of the polarimetric features at the tree nodes. Except the interpretability, decision tree approach could be transplanted to other data set without training process for the same terrain types, since the polarimetric features are inherently connected to the physical scattering properties. Currently, decision tree based classifiers, typically employ one single polarimetric feature at the nodes of the tree. The idea to increase the number of the polarization features at the decision tree node is expected to improve the classification result, which combine two or more polarimetric features to form a two or high dimension feature space. In this way, the classes which cannot be discriminated with one feature could possibly be separated with the space constructed by several features. However, it also inevitably leads to an increase in the computational burden. In fact, not all nodes require very high-dimensional feature space to achieve high classification precision. Therefore, in this paper we proposed that the dimension of the feature space used in the decision tree nodes is adaptively changed from one to three, due to the separability of the classes under this node. The developed classification method is examined by the classical AIRSAR data in Flevoland area of the Netherlands, as well as GaoFen-3 data in Hulunbuir of China. The experiments show that the classification performance is superior to the fixed dimension feature decision tree methods, with less and reasonable computation time. Besides, the transferability of polarimetric features obtained by decision tree is preliminarily demonstrated in the application to another AIRSAR data.

Background Individuals affected by autonomic dysfunction are at a higher risk of developing hypotension following anesthesia induction. Dynamic pupillometry has previously been employed as a means of assessing autonomic function. This prospective observational study was developed to determine whether pupillary light reflex (PLR) parameters can reliably predict post-induction hypotension (PIH). Methods This study enrolled patients with lower ASA status (I-II) undergoing elective surgery. PLR recordings for these patients prior to anesthesia induction were made with an infrared pupil camcorder, with a computer being used to assess Average Constriction Velocity (ACV), Maximum Constriction Velocity (MCV), and Constriction Ratio (CR). PIH was defined by a > 30% reduction in mean arterial pressure (MAP) or any MAP recording < 65 mmHg for at least 1 min from the time of induction until 10 minutes following intubation. Patients were stratified into PIH and non-PIH groups based on whether or not they developed hypotension. Results This study enrolled 61 total patients, of whom 31 (50.8%) exhibited one or more hypotensive episodes. Patients in the PIH group exhibited significantly smaller ACV (P = 0.003) and MCV values (P < 0.001), as well as a higher CR (P = 0.003). Following adjustment for certain factors (Model 2), MCV was identified as a protective factor for PIH (Odds Ratio: 0.369). Receiver operating characteristic (ROC) analyses revealed that relative to CR (AUC: 0.695, 95% CI: 0.563–0.806; P = 0.004), the reciprocal of MCV (1/MCV) offered greater value as a predictor of PIH (AUC: 0.803,95%CI: 0.681–0.894; P < 0.001). Conclusion These results indicate that pupil maximum constriction velocity is a reliable predictor of post-induction hypotension in individuals of ASA I-II status undergoing elective surgery. Trial registration This study was registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2200057164, registration date: 01/03/2022).

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