{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LHZO2U6FYZUHN4SPLWPUK7RF4P","short_pith_number":"pith:LHZO2U6F","schema_version":"1.0","canonical_sha256":"59f2ed53c5c66876f24f5d9f457e25e3f331471949b3dc87eb58907a00a52d71","source":{"kind":"arxiv","id":"2606.04710","version":1},"attestation_state":"computed","paper":{"title":"Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atharva Satam, Maitreya Shelare, Poonam Sonar, Sneha Burnase","submitted_at":"2026-06-03T10:41:50Z","abstract_excerpt":"This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts. The main contribution of this work lies in the feature extraction process and architectural enhancement. Factor Analysis is used for dimensionality reduction, offering improved latent featu"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.04710","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T10:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"678667642e029b075633244f8bed40a773800a2e91163975cc2117f0975d00d6","abstract_canon_sha256":"022e9e19ce11cef92f6d844a07c87c3f3e1ae7c245391374444347ee329a7eba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:26.101522Z","signature_b64":"rQ/XTtTDc7pRPzNi9gXTRoCGXqr5zWHU++QPOr5VEDGyntwRSvfTObIROyCUysq4j0noHOeFd5G/8hIIYyMrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59f2ed53c5c66876f24f5d9f457e25e3f331471949b3dc87eb58907a00a52d71","last_reissued_at":"2026-06-04T01:09:26.100661Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:26.100661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Atharva Satam, Maitreya Shelare, Poonam Sonar, Sneha Burnase","submitted_at":"2026-06-03T10:41:50Z","abstract_excerpt":"This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts. The main contribution of this work lies in the feature extraction process and architectural enhancement. Factor Analysis is used for dimensionality reduction, offering improved latent featu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04710","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.04710/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.04710","created_at":"2026-06-04T01:09:26.100782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.04710v1","created_at":"2026-06-04T01:09:26.100782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04710","created_at":"2026-06-04T01:09:26.100782+00:00"},{"alias_kind":"pith_short_12","alias_value":"LHZO2U6FYZUH","created_at":"2026-06-04T01:09:26.100782+00:00"},{"alias_kind":"pith_short_16","alias_value":"LHZO2U6FYZUHN4SP","created_at":"2026-06-04T01:09:26.100782+00:00"},{"alias_kind":"pith_short_8","alias_value":"LHZO2U6F","created_at":"2026-06-04T01:09:26.100782+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P","json":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P.json","graph_json":"https://pith.science/api/pith-number/LHZO2U6FYZUHN4SPLWPUK7RF4P/graph.json","events_json":"https://pith.science/api/pith-number/LHZO2U6FYZUHN4SPLWPUK7RF4P/events.json","paper":"https://pith.science/paper/LHZO2U6F"},"agent_actions":{"view_html":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P","download_json":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P.json","view_paper":"https://pith.science/paper/LHZO2U6F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.04710&json=true","fetch_graph":"https://pith.science/api/pith-number/LHZO2U6FYZUHN4SPLWPUK7RF4P/graph.json","fetch_events":"https://pith.science/api/pith-number/LHZO2U6FYZUHN4SPLWPUK7RF4P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P/action/storage_attestation","attest_author":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P/action/author_attestation","sign_citation":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P/action/citation_signature","submit_replication":"https://pith.science/pith/LHZO2U6FYZUHN4SPLWPUK7RF4P/action/replication_record"}},"created_at":"2026-06-04T01:09:26.100782+00:00","updated_at":"2026-06-04T01:09:26.100782+00:00"}