Comparative study of popular remote sensing teaching and research software for land use classification in a complex mine scene

doi:10.6046/zrzyyg.2023394

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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.

Keywords remote sensing software complex mine land use classification teaching and research neural network

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TP79

Issue Date: 01 July 2025

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	Chengye ZHANG
	Mengyuan LI
	Jianghe XING
	Yuhang QIU

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Chengye ZHANG,Mengyuan LI,Jianghe XING, et al. Comparative study of popular remote sensing teaching and research software for land use classification in a complex mine scene[J]. Remote Sensing for Natural Resources, 2025, 37(3): 9-16.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023394 OR https://www.gtzyyg.com/EN/Y2025/V37/I3/9

Fig.1 The location and remotely-sensed image of the study area

Tab.1

Fig.2 Structure of ODCC

Tab.2 Comparative experiments on pixel-oriented classification

Tab.3 Comparative experiments on object-oriented classification

Tab.4 Parameter settings for different methods

Tab.5 Comparison of accuracy and runtime for pixel-oriented classification

Tab.6 Comparison of accuracy and runtime for object-oriented classification

Fig.3-1 Results of the four classification experiments

Fig.3-2 Results of the four classification experiments

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