{
  "abstract": "Multi-baseline (MB) phase unwrapping (PU) breaks through the limitations of the phase continuity assumption, but existing MBPU algorithms cannot adapt well to noisy environments. A robust reinforcement learning integer programming multi-baseline PU algorithm with JPEG-LS (RL-MBPU) is proposed. First, the cutting plane–pure integer programming cut-selection process is formulated as a sequential reinforcement-learning problem. An efficacy-guided imitation strategy is used to initialize an MBPU-structure-aware single-cut policy, which is further optimized by an actor–critic method using Cut-PAR2-based trajectory returns to reduce the root-node proof cost. Finally, the initial ambiguity number solution is corrected using dynamic median edge detector based on JPEG-LS, and the PU is completed by using the prediction of the left, top, and top-left neighbors of the current pixel and combining it with the local gradient threshold determination. In both simulation and real data experiments, the RL-MBPU algorithm demonstrated stable PU performance, including computational efficiency and noise robustness for interferograms of different sizes. Compared with the minimum-cost flow and cutting plane–pure integer programming algorithms, the RL-MBPU algorithm reduces the root mean square error by approximately 61.2% and 14.5%, respectively, while improving efficiency by approximately 54.1% and 42.4%, respectively. This can further support high-precision interferometric synthetic aperture radar applications.",
  "abstract_shown": true,
  "affiliations": [
    "College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China",
    "Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China",
    "College of Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China",
    "Yunnan Communications Investment & Construction Group Co., Ltd, Kunming 650000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shiji Yang"
    },
    {
      "name": "Wu Zhu"
    },
    {
      "affs": [
        1
      ],
      "name": "Hui Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Ruixuan Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Haoran Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Lei Xu"
    },
    {
      "affs": [
        2
      ],
      "name": "Mengyuan Zhu"
    },
    {
      "affs": [
        3
      ],
      "name": "Tong Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/79nx7ch95dkvc.bib",
  "cited_by": 0,
  "doi": "10.1016/j.jag.2026.105648",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1569843226005649",
  "id": "79nx7ch95dkvc",
  "issns": [
    "1569-8432",
    "1872-826X"
  ],
  "landing_url": "https://doi.org/10.1016/j.jag.2026.105648",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GB3-5030"
    ],
    [
      "lcc",
      "GE1-350"
    ],
    [
      "openalex",
      "1707|Optical measurement and interference techniques"
    ]
  ],
  "openalex": "W7223179298",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/79nx7ch95dkvc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:37Z",
      "native_id": "dc295c1541cc4601919a05b9e69692bd",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.jag.2026.105648"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:GE1-350",
      "subfield": "environmental-science"
    }
  ],
  "title": "Robust reinforcement learning multi-baseline phase unwrapping algorithm with JPEG-LS",
  "title_text": "Robust reinforcement learning multi-baseline phase unwrapping algorithm with JPEG-LS",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:37Z",
  "url": "https://research.draconitic.com/paper/79nx7ch95dkvc",
  "venue": "International Journal of Applied Earth Observations and Geoinformation",
  "versions": []
}