{"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/red-teaming-models-for-hyperspectral-image","title":"Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI","arxiv_id":"2403.08017","date":"2024-03-12","proceeding":null,"authors":["Vladimir Zaigrajew","Hubert Baniecki","Lukasz Tulczyjew","Agata M. Wijata","Jakub Nalepa","Nicolas Longépé","Przemyslaw Biecek"],"abstract":"Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases. Since both fields advance independently, there is a notable gap in integrating red teaming strategies into RS. This paper introduces a methodology for examining ML models operating on hyperspectral images within the HYPERVIEW challenge, focusing on soil parameters' estimation. We use post-hoc explanation methods from the Explainable AI (XAI) domain to critically assess the best performing model that won the HYPERVIEW challenge and served as an inspiration for the model deployed on board the INTUITION-1 hyperspectral mission. Our approach effectively red teams the model by pinpointing and validating key shortcomings, constructing a model that achieves comparable performance using just 1% of the input features and a mere up to 5% performance loss. Additionally, we propose a novel way of visualizing explanations that integrate domain-specific information about hyperspectral bands (wavelengths) and data transformations to better suit interpreting models for hyperspectral image analysis.","url_abs":"https://arxiv.org/abs/2403.08017v2","url_pdf":"https://arxiv.org/pdf/2403.08017v2.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":[],"tasks":[{"task_slug":"hyperview-challenge","task_name":"HYPERVIEW Challenge"},{"task_slug":"hyperspectral-image-analysis","task_name":"Hyperspectral image analysis"},{"task_slug":"red-teaming","task_name":"Red Teaming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2403.08017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08017"}},"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":"deterministic:regex_extraction","url":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":2,"ran":1,"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":0,"samples":[{"code_sha256_prefix":"f7e0e6ad81b6306f","entry":"relative_mse","repo":"ridvansalihkuzu/hyperview_eagleeyes","repo_kind":"found_in_text","path":"experimental_4/hyperviewnet.py","file_url":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes/blob/HEAD/experimental_4/hyperviewnet.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f7e0e6ad81b6306f"}},{"code_sha256_prefix":"6f95561f783b8039","entry":"HyperviewNet","repo":"ridvansalihkuzu/hyperview_eagleeyes","repo_kind":"found_in_text","path":"experimental_4/hyperviewnet.py","file_url":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes/blob/HEAD/experimental_4/hyperviewnet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6f95561f783b8039"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}