Effect on specific crop mapping using WorldView-2 multispectral add-on bands: soft classification approach

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

“…Moreover, the proposed object-based framework was evaluated against a pixel-based support vector machine (SVM) classification scheme. In particular, recent studies targeting the classification of agricultural landscapes or different crop types [8,22,23,26,56], different tree species [60] and different vine varieties [45,46] were based on SVM classifiers with quite promising results. To this end, we employed a linear SVM classifier from the LIBSVM [61] library and performed similar experiments with exactly the same training samples as in the proposed object-based NN classification process.…”

“…In particular, for every processing step, several experiments were performed based on features employed from the literature on similar crop identification/detection studies (e.g., [8,9,16,[22][23][24]27,30]), while the optimal ones for all datasets were selected from a larger pool, through several experiments, feature analysis tools (assessing how each feature contributes to the discrimination task) and a trial and error procedure for fine tuning their parameters. To this end, Trimble's eCognition Developer (ed.8), MathWorks' MATLAB (2015b) and in-house developed software were employed.…”

“…Regarding the developed vineyard detection methodology, in contrast to similar research efforts towards vineyard and/or other crop identification tasks [8,9,15,[22][23][24]27,30], the proposed approach employs very high resolution multispectral data, along with a specific set of features, rules, segmentation scales and a set of parameters that deliver relatively higher detection rates. Instead of employing only spectral features (e.g., [8,22,23,27]), we employ, as in [9,24], textural features, as well.…”

“…Instead of employing only spectral features (e.g., [8,22,23,27]), we employ, as in [9,24], textural features, as well. However, we solve the problem for very high resolution pan-sharpened data (at 50 cm) instead of the significantly coarser Landsat 8 and ASTER data (15 m/30 m).…”

“…To this end, recent studies have employed high and very high resolution satellite imagery towards vegetation, forest/tree mapping [1][2][3][4], biomass and structural parameters estimation [5][6][7] and crop type mapping and identification [8,9]. Moreover, crop-based and variety-based data analysis can create valuable validated agricultural maps and products for the implementation of effective management decisions [10,11].…”