Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python

Polina Lemenkova
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Abstract

Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. The data are obtained from the United States Geological Survey (USGS) and include the Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) multispectral satellite images. The images were collectedon 2013 and 2023 to evaluate land cover categories in each of the year. The study area covers the region of Nile Delta and the Faiyum Oasis, Egypt. A series of modules for raster image processing was applied using scripting language of GRASS GIS to process the remote sensing data. The satellite images were classified into raster maps presenting the land cover types. These include ‘i.cluster’ and ‘i.maxlik’ for non-supervised classification used as training dataset of random pixel seeds, ‘r.random’, ‘r.learn.train’, ‘r.learn.predict’ and ‘r.category’ for ML part of image processing. The consequences of various ML parameters on the cartographic outputs are analysed, such as speed and accuracy, randomness of nodes, analytical determination of the output weights, and dependence distribution of pixels for each algorithm. Supervised learning models of GRASS GIS were tested and compared including the Gaussian Naive Bayes (GaussianNB), Multi-layer Perceptron classifier (MLPClassifier), Support Vector Machines (SVM) Classifier, and Random Forest Classifier (RF). Though each algorithms was developed to serve different objectives of ML applications in RS data processing, their technical implementation and practical purposes present valuable approaches to cartographic data processing and image analysis. The results shown that the most time-consuming algorithms was noted as SVM classification, while the fastest results were achieved by the GaussianNB approach to image processing and the best results are achieved by RF Classifier.
利用嵌入式Python Scikit-Learn库实现GRASS GIS的ML算法图像处理自动化
利用嵌入Python语言Scikit-Learn库的地理资源分析支持系统(GRASS)地理信息系统GIS算法,研究了机器学习(ML)和人工神经网络(ANN)的图像处理方法。数据来自美国地质调查局(USGS),包括Landsat 8操作陆地成像仪/热红外传感器(OLI/TIRS)多光谱卫星图像。这些图像是在2013年和2023年收集的,用于评估每年的土地覆盖类别。研究区域包括尼罗河三角洲和埃及法尤姆绿洲地区。利用GRASS GIS的脚本语言,应用光栅图像处理的一系列模块对遥感数据进行处理。卫星图像被分类成栅格地图,显示土地覆盖类型。其中包括“i”。群集‘和’ i。Maxlik ‘用于非监督分类,用作随机像素种子的训练数据集,’ r。随机”、“r.learn。”、“r.learn训练。预测‘和’ r。类别'用于图像处理的ML部分。分析了各种ML参数对制图输出的影响,例如速度和准确性、节点的随机性、输出权重的分析确定以及每个算法像素的依赖分布。对GRASS GIS的监督学习模型进行了测试和比较,包括高斯朴素贝叶斯(GaussianNB)、多层感知器分类器(MLPClassifier)、支持向量机(SVM)分类器和随机森林分类器(RF)。虽然每种算法都是为了服务于RS数据处理中ML应用的不同目标而开发的,但它们的技术实现和实际目的为地图数据处理和图像分析提供了有价值的方法。结果表明,SVM分类算法耗时最长,高斯annb方法图像处理速度最快,RF分类器图像处理效果最好。
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