Matches in SemOpenAlex for { <https://semopenalex.org/work/W3033482004> ?p ?o ?g. }
- W3033482004 endingPage "1452" @default.
- W3033482004 startingPage "1437" @default.
- W3033482004 abstract "The rapid increase in the number of remote sensing sensors makes it possible to develop multisource feature extraction and fusion techniques to improve the classification accuracy of surface materials. It has been reported that light detection and ranging (LiDAR) data can contribute complementary information to hyperspectral images (HSIs). In this article, a multiple feature-based superpixel-level decision fusion (MFSuDF) method is proposed for HSIs and LiDAR data classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is first designed and applied to HSIs to both reduce the dimensions and compress the noise impact. Next, 2-D and 3-D Gabor filters are, respectively, employed on the KPCA-reduced HSIs and LiDAR data to obtain discriminative Gabor features, and the magnitude and phase information are both taken into account. Three different modules, including the raw data-based feature cube (concatenated KPCA-reduced HSIs and LiDAR data), the Gabor magnitude feature cube, and the Gabor phase feature cube (concatenation of the corresponding Gabor features extracted from the KPCA-reduced HSIs and LiDAR data), can be, thus, achieved. After that, random forest (RF) classifier and quadrant bit coding (QBC) are introduced to separately accomplish the classification task on the aforementioned three extracted feature cubes. Alternatively, two superpixel maps are generated by utilizing the multichannel simple noniterative clustering (SNIC) and entropy rate superpixel segmentation (ERS) algorithms on the combined HSIs and LiDAR data, which are then used to regularize the three classification maps. Finally, a weighted majority voting-based decision fusion strategy is incorporated to effectively enhance the joint use of the multisource data. The proposed approach is, thus, named MFSuDF. A series of experiments are conducted on three real-world data sets to demonstrate the effectiveness of the proposed MFSuDF approach. The experimental results show that our MFSuDF can achieve the overall accuracy of 73.64%, 93.88%, and 74.11% for Houston, Trento, and Missouri University and University of Florida (MUUFL) Gulport data sets, respectively, when there are only three samples per class for training." @default.
- W3033482004 created "2020-06-12" @default.
- W3033482004 creator A5011329808 @default.
- W3033482004 creator A5013817745 @default.
- W3033482004 creator A5024631382 @default.
- W3033482004 creator A5035753061 @default.
- W3033482004 creator A5038002446 @default.
- W3033482004 creator A5044925704 @default.
- W3033482004 creator A5089103033 @default.
- W3033482004 date "2021-02-01" @default.
- W3033482004 modified "2023-10-16" @default.
- W3033482004 title "Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification" @default.
- W3033482004 cites W1497089125 @default.
- W3033482004 cites W1587559447 @default.
- W3033482004 cites W1965309615 @default.
- W3033482004 cites W1971796065 @default.
- W3033482004 cites W1976359033 @default.
- W3033482004 cites W1991360699 @default.
- W3033482004 cites W1992961908 @default.
- W3033482004 cites W1993707757 @default.
- W3033482004 cites W1997565609 @default.
- W3033482004 cites W1997718749 @default.
- W3033482004 cites W1998030734 @default.
- W3033482004 cites W2015603907 @default.
- W3033482004 cites W2020967408 @default.
- W3033482004 cites W2025803711 @default.
- W3033482004 cites W2049444988 @default.
- W3033482004 cites W2054689043 @default.
- W3033482004 cites W2068873421 @default.
- W3033482004 cites W2072187267 @default.
- W3033482004 cites W2083541351 @default.
- W3033482004 cites W2085529604 @default.
- W3033482004 cites W2087263574 @default.
- W3033482004 cites W2090695002 @default.
- W3033482004 cites W2092923709 @default.
- W3033482004 cites W2102372511 @default.
- W3033482004 cites W2102796633 @default.
- W3033482004 cites W2103384342 @default.
- W3033482004 cites W2106277226 @default.
- W3033482004 cites W2118246710 @default.
- W3033482004 cites W2133185047 @default.
- W3033482004 cites W2135431554 @default.
- W3033482004 cites W2143789670 @default.
- W3033482004 cites W2144966944 @default.
- W3033482004 cites W2148596671 @default.
- W3033482004 cites W2163886442 @default.
- W3033482004 cites W2164821350 @default.
- W3033482004 cites W2293105860 @default.
- W3033482004 cites W2296450878 @default.
- W3033482004 cites W2315409171 @default.
- W3033482004 cites W2342652911 @default.
- W3033482004 cites W2493117470 @default.
- W3033482004 cites W2547026977 @default.
- W3033482004 cites W2557396194 @default.
- W3033482004 cites W2572303978 @default.
- W3033482004 cites W2581808969 @default.
- W3033482004 cites W2586793539 @default.
- W3033482004 cites W2598997103 @default.
- W3033482004 cites W2606929568 @default.
- W3033482004 cites W2610199987 @default.
- W3033482004 cites W2612271937 @default.
- W3033482004 cites W2626015899 @default.
- W3033482004 cites W2765622256 @default.
- W3033482004 cites W2766299485 @default.
- W3033482004 cites W2770149144 @default.
- W3033482004 cites W2781572613 @default.
- W3033482004 cites W2790045795 @default.
- W3033482004 cites W2791254153 @default.
- W3033482004 cites W2796684832 @default.
- W3033482004 cites W2808968757 @default.
- W3033482004 cites W2890133123 @default.
- W3033482004 cites W2945608588 @default.
- W3033482004 cites W2946581458 @default.
- W3033482004 cites W2948256530 @default.
- W3033482004 cites W2951888919 @default.
- W3033482004 cites W2954705316 @default.
- W3033482004 cites W2963181993 @default.
- W3033482004 cites W4300952079 @default.
- W3033482004 cites W4376463272 @default.
- W3033482004 doi "https://doi.org/10.1109/tgrs.2020.2996599" @default.
- W3033482004 hasPublicationYear "2021" @default.
- W3033482004 type Work @default.
- W3033482004 sameAs 3033482004 @default.
- W3033482004 citedByCount "29" @default.
- W3033482004 countsByYear W30334820042021 @default.
- W3033482004 countsByYear W30334820042022 @default.
- W3033482004 countsByYear W30334820042023 @default.
- W3033482004 crossrefType "journal-article" @default.
- W3033482004 hasAuthorship W3033482004A5011329808 @default.
- W3033482004 hasAuthorship W3033482004A5013817745 @default.
- W3033482004 hasAuthorship W3033482004A5024631382 @default.
- W3033482004 hasAuthorship W3033482004A5035753061 @default.
- W3033482004 hasAuthorship W3033482004A5038002446 @default.
- W3033482004 hasAuthorship W3033482004A5044925704 @default.
- W3033482004 hasAuthorship W3033482004A5089103033 @default.
- W3033482004 hasBestOaLocation W30334820042 @default.
- W3033482004 hasConcept C138885662 @default.
- W3033482004 hasConcept C153180895 @default.