A Supervised Segmentation Algorithm for Crop Classification Based on Histograms Using Satellite Images

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

“…We note that the results of the first row of Table 6 and Table 7 differ from those presented in Ref. [28,29]. This is due to, in this results, we only consider sites in the image that correspond to crops in the five categories of interest, i.e., only pixels in the region of interest.…”

“…In this case, we use the matlab built-in function for SVM. In the comparison study we also consider the original version of the probabilistic segmentation approach described in [28,29]. Additionally, for a fair comparison, we include the performance analysis of these algorithms using the best results reached in the feature space study in this work, see Table 6 and Table 7, denoted as MICAI ${2014}^{*}$ and MICAI ${2015}^{*}$ in Table 9.…”

“…Note that the overall accuracy and Kappa coefficient increased in comparison to the Space 1 used in the original paper in Ref. [29]. Similarly, MICAI ${2015}^{*}$ refers to the proposal in [28] using the feature space combination Space 1 and Space 3.…”

“…Observe the similarity of the response among all crops. This fact explains the precision errors of the proposed algorithm in [29]. For addressing this problem the authors in [28] considered using two different feature spaces (sources of information): the feature space composed of green, red and near infrared bands and the space based on the three principal components of 10 vegetation indices [15,16,31,32,33,34,35,36,37].…”

Classification of Land and Crops Based on Satellite Images Landsat 8: Case Study SD Timisoara

“…Classification of satellite images, based on the analysis and distribution on pixel groups with similar spectral characteristics for brightness value, offering high accuracy of image analysis and implicit areas represented (Akgün et al, 2004 Liu et al, 2014). The growing interest in monitoring land and crops on the basis of satellite images has made more research to target these methods, models and algorithms to increase the accuracy of analysis (Hansen et al, 2008;Dalmau and Rivera, 2009, 2011Oliva et al, 2014). There were also analyzed the sources of error in remote sensing analysis of crops distributions, in order to increase accuracy of analysis and certainty of the results, (Ozdogan and Woodcock 2006).…”