{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatial-aware-conformal-prediction-for","title":"Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification","arxiv_id":"2409.01236","date":"2024-09-02","proceeding":null,"authors":["Kangdao Liu","Tianhao Sun","Hao Zeng","Yongshan Zhang","Chi-Man Pun","Chi-Man Vong"],"abstract":"Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this field, they lack the ability to rigorously quantify confidence in their predictions. Quantifying the certainty of model predictions is crucial for the safe usage of predictive models, and this limitation restricts their application in critical contexts where the cost of prediction errors is significant. To support the safe deployment of HSI classifiers, we first provide a theoretical proof establishing the validity of the emerging uncertainty quantification technique, conformal prediction, in the context of HSI classification. We then propose a conformal procedure that equips any trained HSI classifier with trustworthy prediction sets, ensuring that these sets include the true labels with a user-specified probability (e.g., 95\\%). Building on this foundation, we introduce Spatial-Aware Conformal Prediction (\\texttt{SACP}), a conformal prediction framework specifically designed for HSI data. This method integrates essential spatial information inherent in HSIs by aggregating the non-conformity scores of pixels with high spatial correlation, which effectively enhances the efficiency of prediction sets. Both theoretical and empirical results validate the effectiveness of our proposed approach. The source code is available at \\url{https://github.com/J4ckLiu/SACP}.","url_abs":"https://arxiv.org/abs/2409.01236v2","url_pdf":"https://arxiv.org/pdf/2409.01236v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatial-aware-conformal-prediction-for","repo_url":"https://github.com/j4ckliu/sacp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"conformal-prediction","task_name":"Conformal Prediction"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2409.01236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01236"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/j4ckliu/sacp","reach":{"status":"ok"}}],"summary":{"ran":3,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"3810b90c0927ef5a","entry":"assignmentToIndex","repo":"j4ckliu/sacp","repo_kind":"official","path":"libs/utils.py","file_url":"https://github.com/j4ckliu/sacp/blob/HEAD/libs/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"3810b90c0927ef5a"}},{"code_sha256_prefix":"8ff2ebb6700555b2","entry":"indexToAssignment","repo":"j4ckliu/sacp","repo_kind":"official","path":"libs/utils.py","file_url":"https://github.com/j4ckliu/sacp/blob/HEAD/libs/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8ff2ebb6700555b2"}},{"code_sha256_prefix":"59dbac140cecad93","entry":"set_seed","repo":"j4ckliu/sacp","repo_kind":"official","path":"libs/utils.py","file_url":"https://github.com/j4ckliu/sacp/blob/HEAD/libs/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"59dbac140cecad93"}},{"code_sha256_prefix":"bdbf26ec81afb90c","entry":"build_dataloader","repo":"j4ckliu/sacp","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/j4ckliu/sacp/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bdbf26ec81afb90c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}