Matches in SemOpenAlex for { <https://semopenalex.org/work/W3049288510> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W3049288510 abstract "Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks. In recent years, learning-based methods have demonstrated their superior strengths in denoising the HSIs. Unfortunately, most of the methods are manually designed based on the extensive expertise that is not necessarily available to the users interested. In this paper, we propose a novel algorithm to automatically build an optimal Convolutional Neural Network (CNN) to effectively denoise HSIs. Particularly, the proposed algorithm focuses on the architectures and the initialization of the connection weights of the CNN. The experiments of the proposed algorithm have been well-designed and compared against the state-of-the-art peer competitors, and the experimental results demonstrate the competitive performance of the proposed algorithm in terms of the different evaluation metrics, visual assessments, and the computational complexity." @default.
- W3049288510 created "2020-08-21" @default.
- W3049288510 creator A5022855479 @default.
- W3049288510 creator A5051439492 @default.
- W3049288510 creator A5077569089 @default.
- W3049288510 creator A5091058342 @default.
- W3049288510 date "2020-08-14" @default.
- W3049288510 modified "2023-10-16" @default.
- W3049288510 title "Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising" @default.
- W3049288510 cites W1533861849 @default.
- W3049288510 cites W1559956479 @default.
- W3049288510 cites W1568404366 @default.
- W3049288510 cites W1595498733 @default.
- W3049288510 cites W1600877088 @default.
- W3049288510 cites W1686810756 @default.
- W3049288510 cites W1944540851 @default.
- W3049288510 cites W1974438823 @default.
- W3049288510 cites W1994040806 @default.
- W3049288510 cites W2019338222 @default.
- W3049288510 cites W2030927653 @default.
- W3049288510 cites W2056370875 @default.
- W3049288510 cites W2095906131 @default.
- W3049288510 cites W2098477387 @default.
- W3049288510 cites W2100975942 @default.
- W3049288510 cites W2133665775 @default.
- W3049288510 cites W2141473882 @default.
- W3049288510 cites W2141983208 @default.
- W3049288510 cites W2146337213 @default.
- W3049288510 cites W2153663612 @default.
- W3049288510 cites W2163922914 @default.
- W3049288510 cites W2171520281 @default.
- W3049288510 cites W225560312 @default.
- W3049288510 cites W2464748116 @default.
- W3049288510 cites W2528170672 @default.
- W3049288510 cites W2584401907 @default.
- W3049288510 cites W2743606449 @default.
- W3049288510 cites W2805465265 @default.
- W3049288510 cites W2913349789 @default.
- W3049288510 cites W2914736033 @default.
- W3049288510 cites W2949117887 @default.
- W3049288510 cites W2963946985 @default.
- W3049288510 cites W2964046397 @default.
- W3049288510 cites W2964121744 @default.
- W3049288510 cites W2980079746 @default.
- W3049288510 cites W3103919952 @default.
- W3049288510 doi "https://doi.org/10.48550/arxiv.2008.06634" @default.
- W3049288510 hasPublicationYear "2020" @default.
- W3049288510 type Work @default.
- W3049288510 sameAs 3049288510 @default.
- W3049288510 citedByCount "0" @default.
- W3049288510 crossrefType "posted-content" @default.
- W3049288510 hasAuthorship W3049288510A5022855479 @default.
- W3049288510 hasAuthorship W3049288510A5051439492 @default.
- W3049288510 hasAuthorship W3049288510A5077569089 @default.
- W3049288510 hasAuthorship W3049288510A5091058342 @default.
- W3049288510 hasBestOaLocation W30492885101 @default.
- W3049288510 hasConcept C108583219 @default.
- W3049288510 hasConcept C11413529 @default.
- W3049288510 hasConcept C114466953 @default.
- W3049288510 hasConcept C115961682 @default.
- W3049288510 hasConcept C153180895 @default.
- W3049288510 hasConcept C154945302 @default.
- W3049288510 hasConcept C159078339 @default.
- W3049288510 hasConcept C163294075 @default.
- W3049288510 hasConcept C179799912 @default.
- W3049288510 hasConcept C199360897 @default.
- W3049288510 hasConcept C41008148 @default.
- W3049288510 hasConcept C81363708 @default.
- W3049288510 hasConcept C99498987 @default.
- W3049288510 hasConceptScore W3049288510C108583219 @default.
- W3049288510 hasConceptScore W3049288510C11413529 @default.
- W3049288510 hasConceptScore W3049288510C114466953 @default.
- W3049288510 hasConceptScore W3049288510C115961682 @default.
- W3049288510 hasConceptScore W3049288510C153180895 @default.
- W3049288510 hasConceptScore W3049288510C154945302 @default.
- W3049288510 hasConceptScore W3049288510C159078339 @default.
- W3049288510 hasConceptScore W3049288510C163294075 @default.
- W3049288510 hasConceptScore W3049288510C179799912 @default.
- W3049288510 hasConceptScore W3049288510C199360897 @default.
- W3049288510 hasConceptScore W3049288510C41008148 @default.
- W3049288510 hasConceptScore W3049288510C81363708 @default.
- W3049288510 hasConceptScore W3049288510C99498987 @default.
- W3049288510 hasLocation W30492885101 @default.
- W3049288510 hasOpenAccess W3049288510 @default.
- W3049288510 hasPrimaryLocation W30492885101 @default.
- W3049288510 hasRelatedWork W2731899572 @default.
- W3049288510 hasRelatedWork W2732542196 @default.
- W3049288510 hasRelatedWork W2738221750 @default.
- W3049288510 hasRelatedWork W3133861977 @default.
- W3049288510 hasRelatedWork W3156786002 @default.
- W3049288510 hasRelatedWork W3186111093 @default.
- W3049288510 hasRelatedWork W4200173597 @default.
- W3049288510 hasRelatedWork W4214561993 @default.
- W3049288510 hasRelatedWork W4312417841 @default.
- W3049288510 hasRelatedWork W564581980 @default.
- W3049288510 isParatext "false" @default.
- W3049288510 isRetracted "false" @default.
- W3049288510 magId "3049288510" @default.
- W3049288510 workType "article" @default.