2020
DOI: 10.1016/j.heliyon.2020.e05358
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Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Abstract: Radar imagery have few polarization bands which can limit the ability to do traditional digital classification. Harmonization of Sentinel-1 and Landsat 8 data despite having complementary texture information can be a challenge. The objectives of this paper are to explore texture features derived from Landsat 8 OLI and dualpolarized Sentinel-1 SAR speckle filtered and unfiltered backscatter, to aggregate classification results using Decision-Level Fusion (DLF), and to evaluate the performance of decision-level … Show more

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Cited by 32 publications
(20 citation statements)
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“…Decision rules [ 74 ], majority rules [ 46 , 117 , 121 ], and model output averaging [ 30 ] are all relatively simple techniques applied in high-level fusion with the purpose of combining the information extracted by different models. Because model selection tends to have a greater impact on the fusion effectiveness, the combination of the model outputs is usually carried out using one of these standard approaches.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Decision rules [ 74 ], majority rules [ 46 , 117 , 121 ], and model output averaging [ 30 ] are all relatively simple techniques applied in high-level fusion with the purpose of combining the information extracted by different models. Because model selection tends to have a greater impact on the fusion effectiveness, the combination of the model outputs is usually carried out using one of these standard approaches.…”
Section: Discussionmentioning
confidence: 99%
“…Aerial data (collected using UAVs) is used mostly for detection of certain objects (e.g., certain plant species and fruits) [ 38 ] and for estimation of agricultural variables (e.g., soil moisture and nitrogen content) [ 39 , 40 , 41 ]. Satellite data are used for mapping variables as diverse as soil moisture [ 42 , 43 , 44 ], crop type [ 45 , 46 , 47 , 48 , 49 , 50 ], crop phenological states [ 51 , 52 ], evapotranspiration [ 40 , 53 , 54 , 55 , 56 , 57 , 58 ], nitrogen status [ 59 , 60 , 61 , 62 ], biomass [ 63 , 64 ], among others. While most data fusion approaches only use data in the same scale, a few studies have applied data originating from different scales [ 10 , 26 , 28 , 31 , 38 , 40 , 51 , 52 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ].…”
Section: Introductionmentioning
confidence: 99%
“…In the existing classification research, texture features are often used in object-based image analysis (OBIA) [16,40,41], but they are rarely used in pixel-based image analysis. Nevertheless, the research of Chen et al [42] showed that texture features play a role in promoting image fusion and classification in pixel-based research. Zhang et al [43] used the spectral and texture features obtained by Landsat 5 to classify complex areas using an RF classifier, showing that the method has the potential to improve land cover classification accuracy.…”
Section: Author Study Area Smallest Unit Classifier Satellitementioning
confidence: 99%
“…Within the scope of the field, the type of crop is generally single and has good homogeneity, so adding texture features is helpful to distinguish the field from other categories. Calculating texture features by generating a gray level co-occurrence matrix (GLCM) is a representative statistical method generated by calculating the pixel pairs' frequency with specific values and specific spatial relationships in the image [42]. GLCM can be understood as the number distribution table of the pairwise combinations of all different element values in the original matrix, the order of which is equal to the gray level of the original matrix.…”
Section: Texture Featuresmentioning
confidence: 99%
“…The combination of either Sentinel-1 images data and Sentinel-2 optical data for land cover and crop mapping was applied in several countries or areas, such as Belgium (Van Tricht et al, 2018), the Chennai basin in India (Steinhausen et al, 2018), northern Malawi (Kpienbaareh et al, 2021) and the plain of Haouz which is a semi-arid area in Morocco (Moumni and Lahrouni, 2021). S1-data was also combined with other optical data such as Landsat-8 OLI (Chen et al, 2020;Kussul et al, 2016).…”
Section: Introductionmentioning
confidence: 99%