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 fused maps. Gray Level Cooccurrence Matrix (GLCM) is employed to derive sets of seven texture features for Landsat 8 bands and VV þ VH backscatter using 5 Â 5, 7 Â 7, 9 Â 9, and 11 Â 11 window sizes. Each texture feature is stacked with a respective source image and classified using Support Vector Machine (SVM). Classified maps from the best three performers from both speckle filtered and unfiltered are aggregated with classified maps from Landsat 8 using plurality voting algorithm and compared using Z-test. Results indicate an overall classification accuracy of 96.02% from DLF images of Landsat and non-speckle filtered maps, whereas Landsat and speckle filtered achieved 94.69%. The best texture information are derived from the blue band followed by the red band, whereas speckle unfiltered textures performed better than speckle filtered textures. We conclude that integration of Landsat 8 and Sentinel-1, either speckle filtered or unfiltered, improves crop classification and speckles do not have statistically significant effects (p ¼ 0.1208).

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

“…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.…”

“…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 ].…”

Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier

“…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.…”

“…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.…”

High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series

“…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).…”