Comparative study of popular remote sensing teaching and research software for land use classification in a complex mine scene
ZHANG Chengye1,2(), LI Mengyuan1, XING Jianghe1(), QIU Yuhang1
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China 2. State Key Laboratory of Coal Fine Exploration and Intelligent Development, China University of Mining and Technology (Beijing), Beijing 100083, China
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.
张成业, 李梦圆, 邢江河, 邱宇航. 遥感主流教学科研软件在复杂矿山场景土地利用分类中的对比研究[J]. 自然资源遥感, 2025, 37(3): 9-16.
ZHANG Chengye, LI Mengyuan, XING Jianghe, QIU Yuhang. Comparative study of popular remote sensing teaching and research software for land use classification in a complex mine scene. Remote Sensing for Natural Resources, 2025, 37(3): 9-16.
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2025, Vol. 37 
), 李梦圆1, 邢江河1(
