Herbal plants zoning using target detection algorithms on time-series of Sentinel-2 multispectral images (Amygdalus Scoparia)

Today, medicinal plants have a special place in the economy and health of a society. Due to the natural growth of many of these products, the necessity of zoning them for optimum and optimal utilization seems necessary. Traditional zoning solutions are not efficient due to their low accuracy and speed, therefore a new approach is needed. Remote sensing data have many applications in various fields including target detection because of their spectral, spatial and temporal information of land surface phenomena. In this paper, target detection methods including Constrained Energy Minimization (CEM), Matched Filtering (MF), Adjusted Spectral Matched Filter (ASMF) and Adaptive Coherence Estimator (ACE) are used to detect Amygdalus Scoparia in Sentinel-2 satellite time series images. In this process, firstly, the filtering of undesirable effects (unlikely areas of plant growth) is eliminated from the time series of images. Then, with the help of hyper heuristic optimization, the optimal features from time-series were identified to reduce the dimension from one hand and increase the detection accuracy from the other hand. The final detection map is generated by weighting the results obtained from each training sample with a different share of the target. The generalizability of the proposed solution was evaluated using the selected optimal features elsewhere, using the ground truth map. The ROC and its sub-area (AUC) are used to evaluate the results. In the optimization phase for feature selection, the AUC index for all detection methods used was greater than 0.99. The best results in this process were obtained by the CEM detection method, which achieved the accuracy of 0.993 and 0.846 in the optimization and independent evaluation, respectively. The results of this study indicate the ability of Sentinel-2 multiplexed time series images to detect targets such as medicinal plants.