Exploring the object-oriented land cover classification based on Landsat and GF data
SHANG Ming1,2(), MA Jie2,3(), LI Yue1, ZHAO Fei4, GU Pengcheng5, PAN Guangyao6, LI Qian2,7, REN Yangyang1,8
1. School of Earth Science and Engineering, Hebei University of Engineering, Handan 056038, China 2. Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China 3. School of Architecture and Art, Hebei University of Engineering, Handan 056038, China 4. China Satellite Communications Co., Ltd., Beijing 100190, China 5. China Land Surveying and Planning Institute, Beijing 100871, China 6. Jinglang Ecological Environment Technology (Wuhan) Co., Ltd., Wuhan 420111, China 7. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China 8. Hebei Geographic Information Group Co., Ltd., Shijiazhuang 050000, China
This study aims to explore the object-oriented classification based on moderate-resolution remote sensing data. Using the Landsat8 OLI, Landsat5 TM, and GF1 data obtained from the northern mountainous area and the southern plain area in Hebei Province, this study compared the land cover classification effects of four classifiers: support vector machine (SVM), random forest (RF), decision tree (DT), and naive Bayes (NB). Moreover, it analyzed the impacts of critical parameters in SVM, RF, and DT on the classification results. The findings indicate that the classification results of the classifiers vary slightly in the two study areas, with their effects decreased in the order of SVM, NB, RF, and DT. The classification accuracies of SVM and DT fluctuated significantly with parameter changes. With C values not below 103 and gamma values not exceeding 10-1, SVM can yield classification accuracies above 90% in all cases. With depth values over 3, DT exhibits relatively high and stable classification accuracies. With parameter changes, RF manifests slightly varying classification accuracies with nonsignificant variation patterns. The results of this study serve as a reference for exploring the object-oriented land cover classification based on moderate-resolution remote sensing data.
尚明, 马杰, 李悦, 赵菲, 顾鹏程, 潘光耀, 李倩, 任阳阳. Landsat和GF数据面向对象土地覆盖分类研究[J]. 自然资源遥感, 2024, 36(3): 240-247.
SHANG Ming, MA Jie, LI Yue, ZHAO Fei, GU Pengcheng, PAN Guangyao, LI Qian, REN Yangyang. Exploring the object-oriented land cover classification based on Landsat and GF data. Remote Sensing for Natural Resources, 2024, 36(3): 240-247.
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2024, Vol. 36 
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