Open source GIS software GRASS releases 8.0 to 8.4 have received some long overdue improvements in imagery handling such as the ability to reuse spectral signature files of existing classifiers, machine readable output of accuracy assessment tool and a support vector machine (SVM) classifier. Practical comparison of all three pixel-based classifiers of GRASS GIS indicated that the maximum likelihood discriminant analysis classifier is the fastest and least accurate one, followed by a sequential maximum a posteriori classifier with reasonably fast execution time and good accuracy. The newly added SVM classifier is the slowest one but provides the highest accuracy and also shows the highest improvement potential by hyperparameter optimization. To illustrate the capabilities of the core classifiers, a showcase is presented where a single Sentinel 2 image is classified to distinguish 18 crop types using all three classifiers available in GRASS.

中文翻译：

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利用支持向量机改进 GRASS GIS 基于像素的分类

开源 GIS 软件 GRASS 版本 8.0 至 8.4 在图像处理方面获得了一些迟来的改进，例如重用现有分类器的光谱特征文件的能力、精度评估工具的机器可读输出和支持向量机 (SVM) 分类器。GRASS GIS 的所有三种基于像素的分类器的实际比较表明，最大似然判别分析分类器是最快且最不准确的分类器，其次是具有相当快的执行时间和良好准确性的顺序最大后验分类器。新添加的 SVM 分类器是最慢的分类器，但提供了最高的准确度，并且通过超参数优化也显示出最高的改进潜力。为了说明核心分类器的功能，我们展示了一个展示，其中使用 GRASS 中提供的所有三个分类器对单个 Sentinel 2 图像进行分类，以区分 18 种作物类型。