基于卫星遥感图像的农作物分类算法

为提高遥感图像对农作物的预估精度和农业种植效率, 设计了基于卫星遥感图像的农作物分类算法。 以2018 年 7 月 30 日哨兵二号(Sentinel-2)卫星拍摄的高分辨率哈尔滨市农业示范基地卫星影像为实验数据, 在不同光谱波段内(含红边波段), 通过使用最大似然法、 支持向量机法、 神经网络法分别对影像中水稻、 大豆、玉米、 高粱等农作物特征进行提取、 分类, 获得到农作物分类图;将统计结果与真实的参数进行比较, 分析了相同算法下使用不同数据源, 不同算法使用相同数据源, 这两种情况下的分类精度与可靠性。 实验结果表明,通过神经网络法得到的分类结果精度最高, 可靠性最强, 适合于全国范围内推广。

Crop Classification Algorithm Based on Satellite Remote Sensing Image

In order to improve the precision of remote sensing image for crop prediction and the efficiency of agricultural planting, combined with the innovation and entrepreneurship training program of Jilin University, an experimental project of crop classification algorithm based on satellite remote sensing image is designed. Taking the high-resolution satellite image of Harbin agricultural demonstration base captured by sentinel-2 on July 30, 2018 as the experimental data, the characteristics of rice, soybean, corn and sorghum in the image are extracted and classified by using the maximum likelihood method, support vector machine method and neural network method in different spectral bands ( including red band) , and then the crop classification map is obtained, the statistical results are compared with the real parameters, and then the classification accuracy and reliability of different algorithms are compared. The experimental results show that the neural network method has the highest accuracy and the strongest reliability, and is suitable for nationwide promotion.