{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:31:27Z","timestamp":1789659087868,"version":"4.0.1"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Firat University Scientific Research Projects Coordination","award":["MF.25.21"],"award-info":[{"award-number":["MF.25.21"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"crossref"}]},{"award":["MF.25.21"],"award-info":[{"award-number":["MF.25.21"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In hyperspectral image classification, the most suitable classifier depends on the magnitude and the type of degradation present in the scene; this information, however, cannot be obtained without labels at the stage where the choice has to be made. A two-layer diagnosis computed before classification and without labels is proposed in this study: a severity index reports the magnitude of the degradation, while three scene-derived indicators separate independent, band-dependent, spectrally correlated and striped structures from one another, while classification itself remains supervised. The approach is evaluated on ten benchmark scenes under a leakage-aware spatial protocol. Classifier fragility is found to follow two opposing regimes: tree ensembles are robust to spatially local degradation but fragile to degradation spread across the spectrum, whereas the opposite is observed for the convolutional network. Regime-based classifier selection produces the better classification map in every combination examined. Replacing the spatial protocol with a random pixel split is further shown to inflate accuracy by between 0.162 and 0.249 and to change the best classifier in six of the ten scenes.<\/jats:p>","DOI":"10.3390\/s26185837","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:31:57Z","timestamp":1789464717000},"page":"5837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Label-Free Degradation Diagnosis for Classifier Selection in Hyperspectral Scenes"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5343-226X","authenticated-orcid":false,"given":"Cagri","family":"Kaymak","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, Faculty of Engineering, Firat University, 23119 Elazig, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Cem","family":"Atilgan","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Faculty of Technology, Kirklareli University, 39020 Kirklareli, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MSP.2013.2279179","article-title":"Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods","volume":"31","author":"Tuia","year":"2014","journal-title":"IEEE Signal Process. 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