Abstract: The performance of remote sensing image processing software directly influences the effectiveness and efficiency of teaching and research activities conducted by related workers. Focusing on land use classification in a complex mine scene, this study comparatively investigated the performance of popular remote sensing software including Pixel Information Expert (PIE), Environment for Visualizing Images (ENVI), ERDAS IMAGINE (ERDAS), and eCognition Developer (eCognition), and the self-developed deep learning algorithm. The results show that ENVI yielded the highest overall accuracy (OA) and Kappa coefficient but the lowest classification efficiency in conventional pixel-oriented classification. In contrast, ERDAS exhibited the highest operational efficiency while maintaining relatively high accuracy. eCognition achieved the optimal OA and Kappa coefficient and relatively high operational efficiency in conventional object-oriented classification. The deep convolutional neural network algorithm demonstrated superior accuracy over the classification results of conventional methods. Overall, this study quantitatively revealed the performance of various software on different strategies and methods, providing a scientific basis for related workers to choose appropriate image processing software and improve teaching effect and research efficiency.