Extraction of winter wheat planting area in county regions based on principal component analysis features fused with GF-6 WFV image

doi:10.14088/j.cnki.issn0439-8114.2024.08.034

Abstract

Abstract: In order to obtain the planting information of winter wheat at county level accurately and quickly, Guzhen County of Anhui Province was selected as the research area, aiming at the problems of high cost, low efficiency and complex process of multi-temporal methods. An effective area extraction method based on single temporal GF-6 WFV image principal component analysis and original spectral band normalization fusion was proposed, and K-nearest neighbor algorithm was used for land cover classification. The results showed that the proposed method was superior to the other two benchmark methods of RAW and PDR, and the best effect was achieved when the dimensionality reduction parameter was 3. The overall accuracy and Kappa coefficient were 89.71% and 0.87, respectively. The actual accuracy of the winter wheat extraction area was 98.49%, with a relative error of only 1.51%.

Key words: remote sensing, winter wheat, planting area extraction, principal component analysis feature, GF-6 WFV image, Guzhen County

CLC Number:

S127
TP79

ZHANG Meng, XU Jian-peng, ZHOU Lu-yang, WANG Jie, WANG Zhuang, YUE Wei. Extraction of winter wheat planting area in county regions based on principal component analysis features fused with GF-6 WFV image[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(8): 201-208.

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