Purpose: This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements. Design/methodology/approach: First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency. Findings: The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist. Originality value: The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

Background: Observational studies have suggested at a possible link between keratoconus (KC) and various mental disorders, but the exact direction of causation in these associations remains unclear. This study aims to investigate the potential causal link between KC and three prominent mental conditions: Anxiety, Depression, and Schizophrenia.Methods: Using instrumental variables identified from Genome-wide association study (GWAS) data of European individuals, we conducted bidirectional two-sample Mendelian Randomization (MR) analyses to explore potential causal relationships between KC and the three major mental disorders. We primarily employed the Inverse-Variance Weighted (IVW) method to evaluate causality. In addition, we performed four supplementary MR methods (MR-Egger, Weighted Median, Simple Mode, and Weighted Mode). Furthermore, we conducted various sensitivity analyses to assess heterogeneity, horizontal pleiotropy, and result stability.Results: Our findings did not reveal any concrete evidence of a causal link between KC and the three major mental disorders, namely anxiety, depression, and schizophrenia [anxiety: odds ratio (OR)=0.997, 95% confidence interval (CI)=0.988–1.008, p = 0.621; depression: OR=1.008, 95% CI=0.999–1.017, p = 0.084; schizophrenia: OR=1.002, 95% CI= 0.984–1.020, p = 0.840]. Similarly, the three major mental disorders were not caustically associated with KC [anxiety: OR=1.014, 95% CI=0.635–1.620, p = 0.953; depression: OR=1.109, 95% CI= 0.749–1.643, p = 0.604; schizophrenia: OR= 0.969, 95% CI= 0.884–1.062, p = 0.497]. The sensitivity analyses indicated that the results remained robust, with no signs of pleiotropy or heterogeneity.Conclusions: Our study does not support a genetically determined significant causal connection between KC and the three major mental disorders. The increased occurrence of mental disorders observed in KC patients in observational reports likely arises from factors that can be modified. Further research is warranted to unveil the underlying mechanisms behind the associations observed in observational studies.