At present, due to different classification methods, softwares, and samples used for classification which could introduce various systematic errors, the majority of studies for comparing the advantages and disadvantages of pixel-based and object-based classification are unprecise to a certain degree. To make a better comparison between the pixel-based and object-based approaches, pixel-based and object-based classification methods were adopted to classify the fused image of panchromatic and multispectral images provided by GF-1 satellite in the main urban district of Zhoukou on April 17, 2018, using the same hardware and software environments, classifier, training samples, and verification samples, namely four identical conditions. Subjective and objective evaluations of the pixel-based and object-based classification methods were made. For comparison, three machine learning algorithms including Classification and Regression Tree (CART), Support Vector Machine (SVM), and Random Forest (RF) were used as the classifiers in the pixel-based and object-based classification procedure. Results show that (1) both pixel-based and object-based approaches could recognize the main urban targets, which was consistent with previous research results. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image classification, RF produced the highest overall accuracy (78.02%) and the Kappa coefficient (0.72); for object-based image classification, RF also achieved the highest overall accuracy (93.40%) and the Kappa coefficient (0.92), which demonstrated that RF was the best machine learning algorithm for classifying Zhoukou urban targets; (2) due to similar spectral signature and cross-distribution, the Producer's Accuracy (PA) and User's Accuracy (UA) of building land, and traffic land were lower. However, the object-based classification produced much higher PA and UA than pixel-based classification in classifying building land and transportation land. Taking RF as example, the PA of building land increased from 56.18% to 92.13%, with the UA increasing from 69.44% to 87.23%, and the PA of traffic land increased from 72.15% to 89.87%, with the UA increasing from 72.15% to 92.20%; (3) compared with previous related researches, this paper conducts a more scientific and rigorous evaluation for pixel-based and object-based classification methods under the four identical conditions, which provides valuable references to classify urban targets using high resolution satellite remote sensing images in the future.