Research on the Optimization of Multi-Class Land Cover Classification Using Deep Learning with Multispectral Images

Yichuan Li, Junchuan Yu (), Ming Wang, Minying Xie, Laidian Xi, Yunxuan Pang and Changhong Hou
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Yichuan Li: China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Junchuan Yu: China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Ming Wang: China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Minying Xie: China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Laidian Xi: School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Yunxuan Pang: School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Changhong Hou: School of Geosciences and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China

Land, 2024, vol. 13, issue 5, 1-17

Abstract: With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. This paper presents an in-depth investigation into optimizing multi-class land cover classification using high-resolution multispectral images from Worldview3. We explore various optimization strategies, including refined sampling strategies, data band combinations, loss functions, and model enhancements. Our optimizations led to a substantial increase in the Mean Intersection over Union (mIoU) classification accuracy, improving from a baseline of 0.520 to a final accuracy of 0.709, which represents a 35.2% enhancement. Specifically, by optimizing the classic semantic segmentation network in four key aspects, we improved the mIoU by 15.5%. Further improvements through changes in data combinations, sampling methods, and loss functions led to an overall 17.2% increase in mIoU. The proposed model optimization methods enabled the OUNet to outperform the baseline model by providing more precise edge detection and feature representation, while reducing the model parameters scale. Experimental evidence shows that in the application of multi-class land surface classification, increasing the quantity and diversity of samples, avoiding data imbalance issues, is equally valuable for improving overall classification accuracy as it is for enhancing model performance.

Keywords: deep learning; multispectral; Worldview3; classification; model optimization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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