Enhanced Farmland Extraction from Gaofen-2: Multi-Scale Segmentation, SVM Integration, and Multi-Temporal Analysis

In high-resolution remote sensing images, the combination of complex farmland plot features and limitations of manual and traditional classification methods hinders large-scale, automated, and precise farmland plot extraction. Key challenges include the following: (1) low accuracy and speckled noise (or salt-and-pepper noise) in pixel-based extraction methods; (2) difficulty in determining segmentation parameters for multi-scale algorithms; and (3) uncertainty about the optimal extraction period. This study proposes an object-oriented multi-scale segmentation method combined with a support vector machine, leveraging spectral reflectance, texture, and temporal differences between farmland and non-farmland plots. The method was validated across various types of farmland plots in the Xinbei and Jintan districts of Changzhou City, Jiangsu Province, China. Results indicate that there is (1) superior multi-scale segmentation during vegetative growth; (2) optimal segmentation parameters (scale 59, shape 0.2, compactness 0.6); (3) improved separation of farmland plots from large areas using road samples within farmland; and (4) enhanced extraction accuracy for irregular plots by increasing sample size. This approach effectively improves farmland plot extraction accuracy, supporting crop type identification and advancing digital agricultural management.