{"corpus_id":7738974,"paper_sha":"d7bf4582bc8e83a3ba0c23b41e93f261d100a510","doi":"10.1080/0143116031000139863","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":2165577558,"dblp_id":null,"acl_id":null,"title":"Change detection techniques","year":2004,"publication_date":"2004-06-01","venue":"","journal":{"name":"International Journal of Remote Sensing","pages":"2365 - 2401","volume":"25"},"journal_issn":null,"journal_title":null,"publication_types":["Review"],"pubmed_pub_types":null,"s2_fields_of_study":["Geography","Computer Science","Environmental Science"],"reference_count":281,"citation_count":3236,"influential_citation_count":187,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature. Abbreviations used in this paper 6S second simulation of the satellite signal in the solar spectrum ANN artificial neural networks ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer AVHRR Advanced Very High Resolution Radiometer AVIRIS Airborne Visible/Infrared Imaging Spectrometer CVA change vector analysis EM expectation–maximization algorithm ERS-1 Earth Resource Satellite-1 ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image GIS Geographical Information System GS Gramm–Schmidt transformation J-M distance Jeffries–Matusita distance KT Kauth–Thomas transformation or tasselled cap transformation LSMA linear spectral mixture analysis LULC land use and land cover MODIS Moderate Resolution Imaging Spectroradiometer MSAVI Modified Soil Adjusted Vegetation Index MSS Landsat Multi-Spectral Scanner image NDMI Normalized Difference Moisture Index NDVI Normalized Difference Vegetation Index NOAA National Oceanic and Atmospheric Administration PCA principal component analysis RGB red, green and blue colour composite RTB ratio of tree biomass to total aboveground biomass SAR synthetic aperture radar SAVI Soil Adjusted Vegetation Index SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image TM Thematic Mapper VI Vegetation Index","claims":[{"public_id":"cl_a99cf0740013adf51a5ddbdb81713f99","status":"active","text":"Comparing different algorithms is often necessary in practice to identify the best change detection results for a specific application.","confidence":0.88,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_a99cf0740013adf51a5ddbdb81713f99"},{"public_id":"cl_3e7e1327c845baeb10e5bb818c4074a2","status":"active","text":"Image differencing, principal component analysis, and post-classification comparison are identified as the most common change detection methods in the reviewed literature.","confidence":0.96,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_3e7e1327c845baeb10e5bb818c4074a2"},{"public_id":"cl_c9c2603c19c6b043e065231caf18d661","status":"active","text":"No single change detection approach is optimal or applicable to all cases.","confidence":0.9,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_c9c2603c19c6b043e065231caf18d661"},{"public_id":"cl_b002a25028b14115705400229b16b75a","status":"active","text":"Spectral mixture analysis, artificial neural networks, and integration of geographical information system and remote sensing data have become important techniques for change detection applications in recent years.","confidence":0.92,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_b002a25028b14115705400229b16b75a"}],"concepts":[{"public_id":"co_13b0b2ef60586c4c0723841ad479150d","status":"active","name":"remote sensing data","description":"Data collected from satellite or airborne sensors and used as the primary source for change detection.","types":["data type"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_13b0b2ef60586c4c0723841ad479150d"},{"public_id":"co_308863cf7ab2bb7b50e235978f4a177b","status":"active","name":"post-classification comparison","description":"A change detection approach that compares thematic classifications from different times.","types":["method"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_308863cf7ab2bb7b50e235978f4a177b"},{"public_id":"co_4308ab20bbe854123f2458145c6ab322","status":"active","name":"change detection algorithms","description":"Algorithms designed to detect differences or alterations across remotely sensed observations.","types":["method"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_4308ab20bbe854123f2458145c6ab322"},{"public_id":"co_48692032ee0c51bbacc15bb3c1f9fe74","status":"active","name":"geographical information system and remote sensing data integration","description":"The combined use of GIS data and remote sensing data in change detection applications.","types":["method"],"aliases":["GIS and remote sensing data integration"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_48692032ee0c51bbacc15bb3c1f9fe74"},{"public_id":"co_8ebe11b8a1586fd762588db22113a081","status":"active","name":"spectral mixture analysis","description":"A technique that models pixels as mixtures of multiple spectral components for change detection.","types":["method"],"aliases":["linear spectral mixture analysis","LSMA"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_8ebe11b8a1586fd762588db22113a081"},{"public_id":"co_977956b1064831c3b190b0fbdcb10352","status":"active","name":"principal component analysis","description":"A dimensionality-reduction technique used here as a change detection method.","types":["method"],"aliases":["PCA"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_977956b1064831c3b190b0fbdcb10352"},{"public_id":"co_ad9f2a1902b8501c7bf5b151a0979845","status":"active","name":"image differencing","description":"A change detection method that compares images by subtracting pixel values between dates.","types":["method"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_ad9f2a1902b8501c7bf5b151a0979845"},{"public_id":"co_f2172d510d253c17fafb0a58b294afa6","status":"active","name":"specific application","description":"A particular change detection task or use case with its own data and performance needs.","types":["application context"],"aliases":[],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f2172d510d253c17fafb0a58b294afa6"},{"public_id":"co_f2c9867dfa3f14918719b3bbae796370","status":"active","name":"artificial neural networks","description":"Computational models inspired by neural processing that are used here for change detection.","types":["method"],"aliases":["ANN"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f2c9867dfa3f14918719b3bbae796370"},{"public_id":"co_f8d03997cdaf781ade8b00649d908f40","status":"active","name":"change detection techniques","description":"Methods used to identify and analyze changes in Earth's surface features from remote sensing data.","types":["method family"],"aliases":["change detection approaches"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_f8d03997cdaf781ade8b00649d908f40"}],"external_ids":{"DOI":"10.1080/0143116031000139863","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":2165577558,"DBLP":null,"ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/7738974","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":632185,"paper_uid":"2f965658-f5ab-4053-b76b-c082466e056d","canonical_identity":{"paper_id":632185,"paper_uid":"2f965658-f5ab-4053-b76b-c082466e056d","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/7738974"}