OTB.HyperspectralUnmixing: Estimate abundance maps from an hyperspectral image and a set of endmembers.
The application applies a linear unmixing algorithmto an hyperspectral data cube. This method supposes that the mixture betweenaterials in the scene is macroscopic and simulates a linear mixing model ofspectra.The Linear Mixing Model (LMM) acknowledges that reflectancespectrum associated with each pixel is a linear combination of purematerials in the recovery area, commonly known as endmembers. Endmembers canbe estimated using the VertexComponentAnalysis application.The application allows estimating the abundance maps with several algorithms : * Unconstrained Least Square (ucls) * Image Space Reconstruction Algorithm (isra) * Non-negative constrained * Least Square (ncls) * Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).
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Endmembers canbe estimated using the VertexComponentAnalysis application.The application allows estimating the abundance maps with several algorithms : * Unconstrained Least Square (ucls) * Image Space Reconstruction Algorithm (isra) * Non-negative constrained * Least Square (ncls) * Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "http://www.opengis.net/def/rel/ogc/1.0/execute", "type": "application/json", "title": "Execute End Point", "href": "http://tb17.geolabs.fr:8119/ogc-api/processes/OTB.HyperspectralUnmixing/execution"}, {"rel": "alternate", "type": "text/html", "title": "Execute End Point", "href": "http://tb17.geolabs.fr:8119/ogc-api/processes/OTB.HyperspectralUnmixing/execution.html"}], "inputs": {"in": {"title": "The hyperspectral data cube input", "description": "The hyperspectral data cube input", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "in"}, "out": {"title": "The output abundance map. The abundance fraction are stored in a multispectral image where band N corresponds to the fraction of endmembers N in each pixel.", "description": "The output abundance map. The abundance fraction are stored in a multispectral image where band N corresponds to the fraction of endmembers N in each pixel.", "schema": {"type": "string", "default": "float", "enum": ["uint8", "uint16", "int16", "int32", "int32", "float", "double"]}, "id": "out"}, "ie": {"title": "The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember.", "description": "The endmembers (estimated pure pixels) to use for unmixing. 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Last modified: Sat Feb 19 15:43:34 CET 2022