The preparation of accurate LULC is of great importance as it is used in various studies ranging from change detection to geospatial modelling. Literature offers studies comparing different classification algorithms/approaches to prepare LULC maps. However, still there is a lack of studies that can provide a comprehensive analysis on widely used classification algorithms. Hence, in the present study, nine different pixel- and object-based classification algorithms have been used to compare their relative effectiveness in generating remotely sensed LULC maps. The algorithms include maximum likelihood, neural network, support vector machine (linear, polynomial, RBF (radial basis function), sigmoid kernels), random forest (RF) and Naive Bayes for pixel-based classification and maximum likelihood algorithm for object-based classification (OBC) approach. Additionally, the study has analysed the impact of different types of satellite datasets (i.e., high resolution image and resolution merged images of same resolution) on relative effectiveness of the algorithms in classifying the satellite imageries accurately. High resolution (5 m) satellite image LISS 4 MX70, resolution merged satellite images (5 m) LISS 3 merged with LISS 4 mono and LISS 3 merged with IRS-1D are employed for classification. 27 LULC maps (9 classification algorithms * 3 images) are evaluated for comparing classification algorithms. The accuracy assessment of the images is carried out using confusion matrix and Mc Nemar’s test. It has been observed that (1) the performance of all classification algorithms differs from each other and (2) resolution merged data impacts classification accuracy differently when compared to other satellite image of same spatial resolution. RF and OBC are identified as potential classifiers with majority of datasets. The results suggest that due to heterogeneity in urban land-use, it is difficult to achieve higher overall accuracy in classifying a large urban area using 5 m resolution satellite dataset. Moreover, visual examination of LULC should be considered for choosing better classification approach as pixel-based approach produces salt-pepper effect in LULC, whereas OBC produces visually smoothened LULC and identifies even smaller objects in urban landscape. The comparative evaluation of different image types reveal that RF performs better with all images, however, the performance of OBC was found to be improved with original high-resolution data.

准备准确的 LULC 非常重要，因为它用于从变化检测到地理空间建​​模的各种研究。文献提供了比较不同分类算法/方法来准备 LULC 地图的研究。然而，仍然缺乏能够对广泛使用的分类算法进行全面分析的研究。因此，在本研究中，已使用九种不同的基于像素和基于对象的分类算法来比较它们在生成遥感 LULC 地图方面的相对有效性。算法包括最大似然、神经网络、支持向量机（线性、多项式、RBF（径向基函数）、sigmoid核）、随机森林 (RF) 和朴素贝叶斯用于基于像素的分类和最大似然算法用于基于对象的分类 (OBC) 方法。此外，该研究还分析了不同类型的卫星数据集（即高分辨率图像和相同分辨率的分辨率合并图像）对算法准确分类卫星图像的相对有效性的影响。高分辨率（5 m）卫星图像LISS 4 MX70，分辨率合并卫星图像（5 m）与LISS 4 mono合并的LISS 3和与IRS-1D合并的LISS 3用于分类。27张LULC图（9个分类算法*3张图片）用于比较分类算法。使用混淆矩阵和 Mc Nemar 检验对图像的准确性进行评估。已经观察到（1）所有分类算法的性能彼此不同；（2）与相同空间分辨率的其他卫星图像相比，分辨率合并数据对分类精度的影响不同。RF 和 OBC 被确定为具有大多数数据集的潜在分类器。结果表明，由于城市土地利用的异质性，在使用 5 m 分辨率卫星数据集对大型城市区域进行分类时，很难达到更高的整体精度。此外，应考虑对 LULC 进行目视检查以选择更好的分类方法，因为基于像素的方法在 LULC 中产生椒盐效应，而 OBC 产生视觉上平滑的 LULC 并识别城市景观中更小的物体。