Year: 2024
Type: article
Abstract: Crop classification using remote sensing technology is highly important for monitoring agricultural resources and managing water usage, especially in water-scarce regions like the Hei River. Crop classification requires a substantial number of labeled samples, but the collection of labeled samples demands significant resources and sample data may not be available for some years. To classify crops in sample-free years in the middle reaches of the Hei River, we generated multisource spectral data (MSSD) based on a spectral library and sample data. We pre-trained a model using labeled samples, followed by fine-tuning the model with MSSD to complete the crop classification for the years without samples. We conduct experiments using three CNN-based deep learning models and a machine learning model (RF). The experimental results indicate that in the model transfer experiments, using a fine-tuned model yields accurate classification results, with overall accuracy exceeding 90%. When the amount of labeled sample data is limited, fine-tuning the model based on MSSD can enhance the accuracy of crop classification. Overall, fine-tuning models based on MSSD can significantly enhance the accuracy of model transfer and reduce the reliance of deep learning models on large-scale sample datasets. The method to classify crops in the middle reaches of the Hei River can provide data support for local resource utilization and policy formulation.
Source: Scientific Reports
Authors: Huazhu Xue, Yongkang Fan, Guotao Dong, Shi He, Yaokang Lian, Wenfei Luan
Institution: Henan Polytechnic University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,590
Award: G7159837916
OpenAlex ID: W4404612860