{"as_of":"2026-08-07T08:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:276d97c4f9c056c30296bdd8c6c54466368aa0e0555be462604e62f684301938","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T14:42:43.952026Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1907.11935/citation-record","integrity":"/paper/1907.11935/integrity","json":"/paper/1907.11935/citation-record.json","paper":"/paper/1907.11935"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Modern trends in hyperspectral image analysis: A review","venue":null,"work_id":"de7cf6cf-106e-4a90-b988-a0deea3d80c2","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:2c0db664d4beb2714da44162864cb55cd31fb7ecdc16b118b7efc27d3793a999","observation_id":"9725b6d5-4cc5-4b4d-9d95-09087b6a7bf5","resolution":{"observed_at":"2026-05-24T14:44:37.581677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Segmentation of hyperspectral images via subtractive clustering and cluster validation using one-class SVMs","venue":null,"work_id":"899f588e-9457-445c-88c2-5e56ca7a81ba","year":2011},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:1fdf37e7e841d7add4928260ba254bd9bdb156f01b5d9807efc6e4c1a3f780c3","observation_id":"7b883147-776a-4f55-ad24-bfd652579774","resolution":{"observed_at":"2026-05-24T14:44:37.577978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sparse representation-based hyperspectral image classiﬁcation using multiscale superpixels and guided ﬁlter","venue":null,"work_id":"6ac34850-3f69-42af-8d7d-d34891d7642c","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:7a866bc4e63e5dafbf75c54b818914f3dd6c627dfe44fcc5a4033efa49d80c90","observation_id":"13d1932f-dfef-4ace-9699-ec3b81872070","resolution":{"observed_at":"2026-05-24T14:44:37.573622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectralspatial classiﬁcation of hyper- spectral data based on deep belief network","venue":null,"work_id":"4c4e4e4b-91ee-449e-a309-d8b324efc238","year":2015},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:f4e212d563ed00933d6f58a501cf3ce63f8b20cbd7f8c633b1fc9824451fa534","observation_id":"8134de76-958a-4606-b545-89b58806b94d","resolution":{"observed_at":"2026-05-24T14:44:37.569850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectral-spatial feature extraction for hyperspectral image classiﬁcation","venue":null,"work_id":"06dfcb88-3580-493b-924c-4dca76ee29fd","year":2016},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:bdeabf2d58fedbe788d0c30cdba9888c55de1b5bed63fe943d462d842c43b21c","observation_id":"18ca9534-2320-44d9-9d11-0dc994e9a182","resolution":{"observed_at":"2026-05-24T14:44:37.639121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to diversify deep belief networks for hyperspectral image classiﬁcation","venue":null,"work_id":"7f8b72e5-5f4e-474a-a2b2-f8a662566c5c","year":2017},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:57b280e70e0e75e243f4ad1d230d92034b108e96476cb088b0626cdf190c1f89","observation_id":"086fc29b-0abe-4f7a-ae7b-7894e62725e9","resolution":{"observed_at":"2026-05-24T14:44:37.643459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep recurrent nets for hyperspec- tral classiﬁcation","venue":null,"work_id":"bed6dfd5-54ad-4f91-8678-583af607c05c","year":2017},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:6f5cf661ae9fa18419a27dc16b53a31b4fc5adc10c1e14e5ff481c058513c3e5","observation_id":"39064472-f178-451d-8cd7-74deb0573159","resolution":{"observed_at":"2026-05-24T14:44:37.626344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"BASS Net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classiﬁcation","venue":null,"work_id":"58137270-3579-4e02-80b5-e8edf1d415ae","year":2017},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:de414479a11766133f15e8d773021169848debd35460a8348152212a21c52e22","observation_id":"67c36cac-feac-44bb-b389-3a4841c8515c","resolution":{"observed_at":"2026-05-24T14:44:37.630638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Going deeper with contextual CNN for hyper- spectral classiﬁcation","venue":null,"work_id":"5b70d6b5-fde0-4233-9496-4331505a5eb4","year":2017},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:3f9780d1caa504243bb1d4c5f1f10ed394308d10ed98b9718f9a51ebd650798b","observation_id":"9048af14-fda6-4cb5-b9be-ce62259dddc7","resolution":{"observed_at":"2026-05-24T14:44:37.618450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hyperspectral image classiﬁcation using convolutional neural networks and multiple feature learning","venue":null,"work_id":"f0c40805-970b-466b-a336-420a169b60c7","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:5d71383605a44354adef01242e83cdeff62ab69cea868d1b33490d122fd1411e","observation_id":"f893876d-d105-460b-95c3-480da5bdce49","resolution":{"observed_at":"2026-05-24T14:44:37.608406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learning for hyperspectral image classiﬁcation: An overview","venue":null,"work_id":"deed3ddf-7559-45f3-a273-755c91d4ea9e","year":2019},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:0337ae6a2098d011e413e44efd12f9299f9fa57657dc0b58ed409f7191234a6f","observation_id":"47400d36-23de-4f94-b9d8-00c3823a2055","resolution":{"observed_at":"2026-05-24T14:44:37.611997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hyperspectral image classiﬁcation with deep learning models","venue":null,"work_id":"c4151d4a-4d55-41d5-bd4b-b94a1295e8a1","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:536f7ad87454ec69bf676baf6a05d37965a09591913dca8208886c5ad03d56ff","observation_id":"a2fcded3-a4fe-4f00-97e2-ed93f7b72b85","resolution":{"observed_at":"2026-05-24T14:44:37.622677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep feature extraction and classiﬁcation of hyperspectral images based on convolutional neural networks","venue":null,"work_id":"3d6a0e58-c9d3-498a-b3f1-365639e44370","year":2016},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:1492a8f72086e91a6893a4e6a87612316da8bc9f24c03a59050d14329f8a7e3a","observation_id":"ace07243-6536-485d-bff3-83c676f90f64","resolution":{"observed_at":"2026-05-24T14:44:37.634681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectralspatial residual network for hyperspectral image classiﬁcation: A 3-d deep learning framework","venue":null,"work_id":"6c9784df-1703-40cc-bdc7-27fd68733af2","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:4025bb644056f7214cac02fa8c26d1f716f441ac172e61eb5a6a6fcb479a70c4","observation_id":"de3b76dd-c7e6-41af-8ac9-5011c5da5c20","resolution":{"observed_at":"2026-05-24T14:44:37.593054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"3-d deep learning approach for remote sensing image classiﬁcation","venue":null,"work_id":"c27de323-2aff-4d70-80af-276a63cfca34","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:8f1ef204ed9c810908496b4264f8e571c07e243c3e9d4a43c8156ec26d36776f","observation_id":"86c48d75-a689-4374-8c81-dc59050328c6","resolution":{"observed_at":"2026-05-24T14:44:37.601226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A new deep convolutional neural network for fast hyperspectral image classiﬁcation","venue":null,"work_id":"2ac472bd-b7f1-4c08-a46f-9dd430d30b7f","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:e29dfa56d4b12e5b97428b2cb67717f515824f8674dd3aa539a3f80d448c8c7e","observation_id":"f0d17bf2-4977-455a-a830-4c1ee17fc202","resolution":{"observed_at":"2026-05-24T14:44:37.597115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Validating hyperspectral image segmentation","venue":null,"work_id":"1b5a1edc-16c3-4d9f-bbeb-4913ffbf3624","year":2019},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:9fb3ac7033f44bd728d31c4f333a41ea6d3ff662abef3101f87159207d681dc8","observation_id":"4b899278-a902-4a52-9b92-f05dce313aa5","resolution":{"observed_at":"2026-05-24T14:44:37.604963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unsupervised spectralspatial feature learning via deep residual convdeconv network for hyperspectral image classiﬁcation","venue":null,"work_id":"97acfadf-82a9-426e-aaff-84c8f61399ce","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:459890c5acfb0dd3508e870a54f1643b4a199efe82677c9e31dbb371ff466fd8","observation_id":"80fd2cc1-a2be-4c36-93e2-4a6f700808b2","resolution":{"observed_at":"2026-05-24T14:44:37.585559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.08870","last_updated":"2019-07-20T22:17:10Z","snapshot_observed_at":"2026-07-06T08:09:02.610918Z","submitted_at":"2019-07-20T22:17:10Z","title":"Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders","version":1},"cited_work":{"arxiv_id":"1907.08870","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1907.08870","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unsupervised segmentation of hyperspectral images using 3D convo- lutional autoencoders","venue":null,"work_id":"36f12b82-c708-45ea-84fe-aac87004890b","year":1907},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"cited_paper":"/paper/1907.08870","citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:5808c80b81560332a9fe82308d1859a13a9881b006ba34ff7026fbcfe44b14c4","observation_id":"19c7c586-5a5d-4646-a607-cfca25a8e485","resolution":{"observed_at":"2026-05-24T14:44:36.372409Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Training- and test-time data augmentation for hyperspectral image segmentation","venue":null,"work_id":"29338915-1fa4-4f64-9aab-6abdea145262","year":2019},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:4f8aff53e354d506894fcc0f5d05637b727bdcc74671d9d5ca0b8bff3d513e17","observation_id":"4c9f1262-2963-44d9-9dbb-c7d37e83319a","resolution":{"observed_at":"2026-05-24T14:44:37.588829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Active learning with convolutional neural networks for hyperspectral image classiﬁcation using a new bayesian approach","venue":null,"work_id":"35f12430-cb7d-43a2-ab29-72dcbb0e1e62","year":2018},"citing_paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T14:42:43.952026Z"},"links":{"citing_paper":"/paper/1907.11935"},"observation_digest":"sha256:e0d430660c62e1d6236ca8302279f9236238b037805003f10732c4e162c67e17","observation_id":"0bf3174e-756e-415d-bcca-f716dc8ba36d","resolution":{"observed_at":"2026-05-24T14:44:37.647518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1907.11935","last_updated":"2019-07-27T15:32:10Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-01T18:24:11.322795Z","submitted_at":"2019-07-27T15:32:10Z","title":"Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":21},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:1907.11935."}