Matches in SemOpenAlex for { <https://semopenalex.org/work/W3134587890> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W3134587890 endingPage "5" @default.
- W3134587890 startingPage "1" @default.
- W3134587890 abstract "We report on a methodological framework that analyzes land-use images with engineered (manually designed) features. As older feature engineering methods suffered from excessive computation of their features, we therefore, introduce techniques that are faster and also more elaborate than any previous approach. Feature extraction and description is based on contour and region information. The contour analysis comprises the detection of ridge, river, and edge contours and is based on a technique of minimal complexity (without requiring costly multiplicative operations). The traced contour segments are then partitioned and abstracted; then they are clustered to form group descriptors. The region analysis consists of the detection of brighter, darker, and flatter regions, as well as regions obtained from clustering; the clustering is carried out with minimal complexity using a hierarchical analysis. Region segments are partitioned and abstracted using a fast implementation of the symmetric-axis transform. A total of ca. 70 parameters is developed and land-use classification experiments are performed. On the UC Merced (UCMD) collection, the classification accuracy of Deep Nets is reached; on the NWPU-RESISC45 collection the accuracy still lags somewhat." @default.
- W3134587890 created "2021-03-15" @default.
- W3134587890 creator A5051852119 @default.
- W3134587890 date "2022-01-01" @default.
- W3134587890 modified "2023-09-30" @default.
- W3134587890 title "Land Use Classification With Engineered Features" @default.
- W3134587890 cites W1912954554 @default.
- W3134587890 cites W1968591910 @default.
- W3134587890 cites W1971025369 @default.
- W3134587890 cites W1980038761 @default.
- W3134587890 cites W1984792953 @default.
- W3134587890 cites W2001123951 @default.
- W3134587890 cites W2006603039 @default.
- W3134587890 cites W2010893982 @default.
- W3134587890 cites W2080478729 @default.
- W3134587890 cites W2120627761 @default.
- W3134587890 cites W2131435419 @default.
- W3134587890 cites W2140620048 @default.
- W3134587890 cites W2154823510 @default.
- W3134587890 cites W2291068538 @default.
- W3134587890 cites W2411876745 @default.
- W3134587890 cites W2506992526 @default.
- W3134587890 cites W2769006839 @default.
- W3134587890 cites W2783165089 @default.
- W3134587890 cites W2940726923 @default.
- W3134587890 cites W2957881196 @default.
- W3134587890 cites W2963108767 @default.
- W3134587890 cites W3014295168 @default.
- W3134587890 cites W3022140654 @default.
- W3134587890 cites W3087867645 @default.
- W3134587890 cites W3103856189 @default.
- W3134587890 cites W946771493 @default.
- W3134587890 doi "https://doi.org/10.1109/lgrs.2020.3046308" @default.
- W3134587890 hasPublicationYear "2022" @default.
- W3134587890 type Work @default.
- W3134587890 sameAs 3134587890 @default.
- W3134587890 citedByCount "0" @default.
- W3134587890 crossrefType "journal-article" @default.
- W3134587890 hasAuthorship W3134587890A5051852119 @default.
- W3134587890 hasConcept C11413529 @default.
- W3134587890 hasConcept C124101348 @default.
- W3134587890 hasConcept C134306372 @default.
- W3134587890 hasConcept C138885662 @default.
- W3134587890 hasConcept C153180895 @default.
- W3134587890 hasConcept C154945302 @default.
- W3134587890 hasConcept C162307627 @default.
- W3134587890 hasConcept C205649164 @default.
- W3134587890 hasConcept C2776401178 @default.
- W3134587890 hasConcept C32277403 @default.
- W3134587890 hasConcept C33923547 @default.
- W3134587890 hasConcept C41008148 @default.
- W3134587890 hasConcept C41895202 @default.
- W3134587890 hasConcept C42747912 @default.
- W3134587890 hasConcept C45374587 @default.
- W3134587890 hasConcept C52622490 @default.
- W3134587890 hasConcept C58640448 @default.
- W3134587890 hasConcept C73555534 @default.
- W3134587890 hasConcept C92835128 @default.
- W3134587890 hasConceptScore W3134587890C11413529 @default.
- W3134587890 hasConceptScore W3134587890C124101348 @default.
- W3134587890 hasConceptScore W3134587890C134306372 @default.
- W3134587890 hasConceptScore W3134587890C138885662 @default.
- W3134587890 hasConceptScore W3134587890C153180895 @default.
- W3134587890 hasConceptScore W3134587890C154945302 @default.
- W3134587890 hasConceptScore W3134587890C162307627 @default.
- W3134587890 hasConceptScore W3134587890C205649164 @default.
- W3134587890 hasConceptScore W3134587890C2776401178 @default.
- W3134587890 hasConceptScore W3134587890C32277403 @default.
- W3134587890 hasConceptScore W3134587890C33923547 @default.
- W3134587890 hasConceptScore W3134587890C41008148 @default.
- W3134587890 hasConceptScore W3134587890C41895202 @default.
- W3134587890 hasConceptScore W3134587890C42747912 @default.
- W3134587890 hasConceptScore W3134587890C45374587 @default.
- W3134587890 hasConceptScore W3134587890C52622490 @default.
- W3134587890 hasConceptScore W3134587890C58640448 @default.
- W3134587890 hasConceptScore W3134587890C73555534 @default.
- W3134587890 hasConceptScore W3134587890C92835128 @default.
- W3134587890 hasLocation W31345878901 @default.
- W3134587890 hasOpenAccess W3134587890 @default.
- W3134587890 hasPrimaryLocation W31345878901 @default.
- W3134587890 hasRelatedWork W1964120219 @default.
- W3134587890 hasRelatedWork W2000165426 @default.
- W3134587890 hasRelatedWork W2144059113 @default.
- W3134587890 hasRelatedWork W2146076056 @default.
- W3134587890 hasRelatedWork W2385132419 @default.
- W3134587890 hasRelatedWork W2546942002 @default.
- W3134587890 hasRelatedWork W2592952084 @default.
- W3134587890 hasRelatedWork W2772780115 @default.
- W3134587890 hasRelatedWork W2811390910 @default.
- W3134587890 hasRelatedWork W3003836766 @default.
- W3134587890 hasVolume "19" @default.
- W3134587890 isParatext "false" @default.
- W3134587890 isRetracted "false" @default.
- W3134587890 magId "3134587890" @default.
- W3134587890 workType "article" @default.