{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:HC7CJ5F4H3N5I67GZDHWIII6QS","short_pith_number":"pith:HC7CJ5F4","canonical_record":{"source":{"id":"1511.02916","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:29:13Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"335220c550bb0d876946a26e978718250610436095c28f5ca41f474621d2a615","abstract_canon_sha256":"f367af1b68bbe0cca8aba81259a4baf6770eee5bfa79332482997970c313a8c7"},"schema_version":"1.0"},"canonical_sha256":"38be24f4bc3edbd47be6c8cf64211e84896dd9be5345e60f4ce453b7d2c084bf","source":{"kind":"arxiv","id":"1511.02916","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02916","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02916v1","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02916","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"HC7CJ5F4H3N5","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"HC7CJ5F4H3N5I67G","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"HC7CJ5F4","created_at":"2026-05-18T12:29:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:HC7CJ5F4H3N5I67GZDHWIII6QS","target":"record","payload":{"canonical_record":{"source":{"id":"1511.02916","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:29:13Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"335220c550bb0d876946a26e978718250610436095c28f5ca41f474621d2a615","abstract_canon_sha256":"f367af1b68bbe0cca8aba81259a4baf6770eee5bfa79332482997970c313a8c7"},"schema_version":"1.0"},"canonical_sha256":"38be24f4bc3edbd47be6c8cf64211e84896dd9be5345e60f4ce453b7d2c084bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:17.451982Z","signature_b64":"y0l+x2rQ2VYo0VgTq2y8GzjsEW2VKwgzsoFacJcITJwERjPirne+lEAMu8KY31xrX8xVRfNpT8uZOcam7Tz3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38be24f4bc3edbd47be6c8cf64211e84896dd9be5345e60f4ce453b7d2c084bf","last_reissued_at":"2026-05-18T01:27:17.451256Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:17.451256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.02916","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g0D9NC57LF2L4LDjeQzd6pWmqywBfFei+xRujlXGh1j+vTHGlFXwHoXnY1pwgzD7bJJX8jBtBZZsY4HNU9BmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:34:09.081326Z"},"content_sha256":"111dae15628027dcab77ff58716a95399dcbe9fcf51a3b3e44085c06a350e34c","schema_version":"1.0","event_id":"sha256:111dae15628027dcab77ff58716a95399dcbe9fcf51a3b3e44085c06a350e34c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:HC7CJ5F4H3N5I67GZDHWIII6QS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Gang Wang, Xing Zhao, Yushi Chen, Zhouhan Lin","submitted_at":"2015-11-09T22:29:13Z","abstract_excerpt":"Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02916","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y1C7aCXYdIc4mnirlZiIVMZCy98sQAlgpgBOVPkpFurl80fZJF3gbCk79ug2SQk3/CFiW5+vRNCNRLD/W1okDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:34:09.081712Z"},"content_sha256":"0cd0449002d9d01b101781e1c5239e43165e24ab310f9aaec9b96ab14eea6b28","schema_version":"1.0","event_id":"sha256:0cd0449002d9d01b101781e1c5239e43165e24ab310f9aaec9b96ab14eea6b28"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/bundle.json","state_url":"https://pith.science/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T19:34:09Z","links":{"resolver":"https://pith.science/pith/HC7CJ5F4H3N5I67GZDHWIII6QS","bundle":"https://pith.science/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/bundle.json","state":"https://pith.science/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HC7CJ5F4H3N5I67GZDHWIII6QS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:HC7CJ5F4H3N5I67GZDHWIII6QS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f367af1b68bbe0cca8aba81259a4baf6770eee5bfa79332482997970c313a8c7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:29:13Z","title_canon_sha256":"335220c550bb0d876946a26e978718250610436095c28f5ca41f474621d2a615"},"schema_version":"1.0","source":{"id":"1511.02916","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02916","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02916v1","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02916","created_at":"2026-05-18T01:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"HC7CJ5F4H3N5","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"HC7CJ5F4H3N5I67G","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"HC7CJ5F4","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:0cd0449002d9d01b101781e1c5239e43165e24ab310f9aaec9b96ab14eea6b28","target":"graph","created_at":"2026-05-18T01:27:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine ","authors_text":"Gang Wang, Xing Zhao, Yushi Chen, Zhouhan Lin","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:29:13Z","title":"Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02916","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:111dae15628027dcab77ff58716a95399dcbe9fcf51a3b3e44085c06a350e34c","target":"record","created_at":"2026-05-18T01:27:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f367af1b68bbe0cca8aba81259a4baf6770eee5bfa79332482997970c313a8c7","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-09T22:29:13Z","title_canon_sha256":"335220c550bb0d876946a26e978718250610436095c28f5ca41f474621d2a615"},"schema_version":"1.0","source":{"id":"1511.02916","kind":"arxiv","version":1}},"canonical_sha256":"38be24f4bc3edbd47be6c8cf64211e84896dd9be5345e60f4ce453b7d2c084bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38be24f4bc3edbd47be6c8cf64211e84896dd9be5345e60f4ce453b7d2c084bf","first_computed_at":"2026-05-18T01:27:17.451256Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:27:17.451256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y0l+x2rQ2VYo0VgTq2y8GzjsEW2VKwgzsoFacJcITJwERjPirne+lEAMu8KY31xrX8xVRfNpT8uZOcam7Tz3CQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:27:17.451982Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.02916","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:111dae15628027dcab77ff58716a95399dcbe9fcf51a3b3e44085c06a350e34c","sha256:0cd0449002d9d01b101781e1c5239e43165e24ab310f9aaec9b96ab14eea6b28"],"state_sha256":"467f88ec32637b6c32bd8380d5a04cebffa60c389cce37551f53adf88faf4053"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gZYpf/VsDByb+qlcDh7Y5nfJrYDIKfrLwNZK25gYW2vG4woJWV0yAKTebcnvxZbBDrJokeA+6IfiQfpN5LJnCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:34:09.084279Z","bundle_sha256":"ea48f6bffa65e53257685f8d753786bba470ba8ea0307390355d709ebe7af502"}}