{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:XX7PE7ZKSVUMTQVEM5NGC4I43P","short_pith_number":"pith:XX7PE7ZK","canonical_record":{"source":{"id":"2011.11005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-22T12:50:08Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"d7350d91a10c34de6275c24901bb2c7ed14090496da52e6c733bcbeb0149d678","abstract_canon_sha256":"5892544714ee993d8eca657e62ceb2aef0af46078696a1dbfb613e7056980499"},"schema_version":"1.0"},"canonical_sha256":"bdfef27f2a9568c9c2a4675a61711cdbcc556e6826a91849ff6fbe371f28dcbd","source":{"kind":"arxiv","id":"2011.11005","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.11005","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"arxiv_version","alias_value":"2011.11005v1","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.11005","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_12","alias_value":"XX7PE7ZKSVUM","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_16","alias_value":"XX7PE7ZKSVUMTQVE","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_8","alias_value":"XX7PE7ZK","created_at":"2026-07-05T01:53:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:XX7PE7ZKSVUMTQVEM5NGC4I43P","target":"record","payload":{"canonical_record":{"source":{"id":"2011.11005","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-22T12:50:08Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"d7350d91a10c34de6275c24901bb2c7ed14090496da52e6c733bcbeb0149d678","abstract_canon_sha256":"5892544714ee993d8eca657e62ceb2aef0af46078696a1dbfb613e7056980499"},"schema_version":"1.0"},"canonical_sha256":"bdfef27f2a9568c9c2a4675a61711cdbcc556e6826a91849ff6fbe371f28dcbd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:53:28.620873Z","signature_b64":"AmBYcF3hFb7P4rBOMVXbx2kb1vSeJCJTR9RYWb/9cHmoazX8pIsFd/QTnYhBPvWv8tIUQgBCtKDEt+c0QFjWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdfef27f2a9568c9c2a4675a61711cdbcc556e6826a91849ff6fbe371f28dcbd","last_reissued_at":"2026-07-05T01:53:28.620430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:53:28.620430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.11005","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":"2026-07-05T01:53:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GhmHFHsxQymNnvnqmzcpCCmlSUsoUiYjigadgl648tqX3120/6BdOV+buazrHeXlRY86CWxTo5Znfajz7mKQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T18:23:39.800093Z"},"content_sha256":"716e8f6708b3d01ffe888ad1046643bfddbb1d0c46a6ac88feadcd2a6fed7b8c","schema_version":"1.0","event_id":"sha256:716e8f6708b3d01ffe888ad1046643bfddbb1d0c46a6ac88feadcd2a6fed7b8c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:XX7PE7ZKSVUMTQVEM5NGC4I43P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Ce Zhang, Hang Su, Peter M. Atkinson, Xiaoheng Tan, Xiaowei Gu, Xinzheng Zhang","submitted_at":"2020-11-22T12:50:08Z","abstract_excerpt":"Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.11005","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/2011.11005/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:53:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L9r+L91sBPZMSsFpJRTnsLjxeYBhOagPA9rPI0cfZmbbr6DLPMrefADNAUNShVGTSWeS8Wr8aRmRHz/bHGtrCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T18:23:39.800614Z"},"content_sha256":"ffa5e1b5ff76e7f3e0339cd43af7dcda391fc435a5c15441cec319f246b81808","schema_version":"1.0","event_id":"sha256:ffa5e1b5ff76e7f3e0339cd43af7dcda391fc435a5c15441cec319f246b81808"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/bundle.json","state_url":"https://pith.science/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/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":"2026-08-13T18:23:39Z","links":{"resolver":"https://pith.science/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P","bundle":"https://pith.science/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/bundle.json","state":"https://pith.science/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XX7PE7ZKSVUMTQVEM5NGC4I43P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:XX7PE7ZKSVUMTQVEM5NGC4I43P","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":"5892544714ee993d8eca657e62ceb2aef0af46078696a1dbfb613e7056980499","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-22T12:50:08Z","title_canon_sha256":"d7350d91a10c34de6275c24901bb2c7ed14090496da52e6c733bcbeb0149d678"},"schema_version":"1.0","source":{"id":"2011.11005","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.11005","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"arxiv_version","alias_value":"2011.11005v1","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.11005","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_12","alias_value":"XX7PE7ZKSVUM","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_16","alias_value":"XX7PE7ZKSVUMTQVE","created_at":"2026-07-05T01:53:28Z"},{"alias_kind":"pith_short_8","alias_value":"XX7PE7ZK","created_at":"2026-07-05T01:53:28Z"}],"graph_snapshots":[{"event_id":"sha256:ffa5e1b5ff76e7f3e0339cd43af7dcda391fc435a5c15441cec319f246b81808","target":"graph","created_at":"2026-07-05T01:53:28Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2011.11005/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchan","authors_text":"Ce Zhang, Hang Su, Peter M. Atkinson, Xiaoheng Tan, Xiaowei Gu, Xinzheng Zhang","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-22T12:50:08Z","title":"Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.11005","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":"sha256:716e8f6708b3d01ffe888ad1046643bfddbb1d0c46a6ac88feadcd2a6fed7b8c","target":"record","created_at":"2026-07-05T01:53:28Z","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":"5892544714ee993d8eca657e62ceb2aef0af46078696a1dbfb613e7056980499","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-22T12:50:08Z","title_canon_sha256":"d7350d91a10c34de6275c24901bb2c7ed14090496da52e6c733bcbeb0149d678"},"schema_version":"1.0","source":{"id":"2011.11005","kind":"arxiv","version":1}},"canonical_sha256":"bdfef27f2a9568c9c2a4675a61711cdbcc556e6826a91849ff6fbe371f28dcbd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdfef27f2a9568c9c2a4675a61711cdbcc556e6826a91849ff6fbe371f28dcbd","first_computed_at":"2026-07-05T01:53:28.620430Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:53:28.620430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AmBYcF3hFb7P4rBOMVXbx2kb1vSeJCJTR9RYWb/9cHmoazX8pIsFd/QTnYhBPvWv8tIUQgBCtKDEt+c0QFjWCw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:53:28.620873Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.11005","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:716e8f6708b3d01ffe888ad1046643bfddbb1d0c46a6ac88feadcd2a6fed7b8c","sha256:ffa5e1b5ff76e7f3e0339cd43af7dcda391fc435a5c15441cec319f246b81808"],"state_sha256":"46339e4026660cad2c3b27bf98b4ec6cf50a86ccb330f65a229cb575ead9a89d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JQGVJq/XOVlb3eGo8xTZDUpyXJVUmlK8Dal57xpVFIBuPA4Sqxi8RoFFm6O0u21yRD3VZeHrZX6NvIxd5DWDBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T18:23:39.807005Z","bundle_sha256":"6c5dda6d3d00fd4b83156e0eb3166051df75e76cf71eb719a9f63cb6e38e5412"}}