基于改进半监督模型的空间异质性农田特征提取研究

Spatially Heterogeneous Cropland Characteristic Extraction Based on Improved Semi-supervised Models

提取农田信息对智慧农业、环境保护等有重要意义。监督学习模型对不同地貌、区域、种植类型等空间异质性农田的特征提取效果不佳。针对该问题,本文提出一种半监督学习模型,该模型使用基于加权损失函数的在线难例样本挖掘策略,在Vaihingen数据集中总体精度高达87.1%,相较于其他半监督学习模型的提取效果最好。在吉林一号农田影像数据集进行空间异质性农田特征提取中的对比试验和精度评估,结果表明:分别使用拟提取地区和训练集地区的无标注影像训练该模型,均可提高对空间异质性农田特征提取精度,若无标注影像与拟提取地区影像中农田特征相似度高,总体精度可提升2.1~6.1个百分点,总体精度最高可达84.0%。该模型使用更少量的标注信息获得媲美监督学习模型的提取效果;而使用相同量的标注信息,可以通过增加无标注影像以取得比监督学习模型更好的提取效果。本文构建河北献县地区的农田数据集,模型使用吉林一号农田影像数据集(部分1)作为有标注训练集,吉林一号农田影像数据集(部分2)和献县地区高分二号影像数据集作为无标注影像训练后的总体精度高达88.7%。验证了改进后的半监督学习模型可准确有效提取空间异质性农田特征。

Extracting cropland accurately and efficiently from high-resolution remote sensing images is of great significance to agricultural production and agricultural resource investigation.Cropland with different areas,ground covers and cultivation types in remote sensing images have large differences in features,whereas the insufficient generalization ability of traditional supervised learning models also leads to poor extraction of heterogeneous cropland with the above features.To solve this problem,the semisupervised semantic segmentation with mutual knowledge distillation(SSS-MKD)model as the base model and incorporated an online hard example mining strategy based on a weighted loss function.The proposed model was evaluated on the Vaihingen dataset and achieved the highest overall accuracy of 87.1%and an average F1 score of 85.0%,The model had the best extraction accuracy compared with other semi-supervised models.In addition,for the task of large-area cropland extraction,the feature information of the unannotated images that were heterogeneous and homogeneous with the annotated images were added to the training of the semi-supervised learning model by designing two sets of experiments using Jilin-1 cropland image dataset,respectively,in order to improve the cropland extraction accuracy in the proposed extraction area.The experimental results showed that the proposed model could achieve the highest overall accuracy of 84.0%by using the cropland images to be extracted for assisted training.In addition,by using unlabeled images with strong similarity to the cropland in the target region,the overall laccuracy could be further improved by 2.i~6.1 percentage points.The maximum overall accuracy achieved using unlabeled images with strong similarity to the cropland features in the training set was 81.6%,which was 6.6~8.5 percentage points higher than the accuracy achieved by using conventional supervised learning.Taking Xian County area in Hebei Province as an example,the model used the Jilin-1 cropland image dataset(part 1)as the labeled training set,and achieved the highest accuracy of 88.7%overall accuracy after training with both the Jilin-1 cropland image dataset(part 2)and the unlabeled images from the Xian County area's Gaofen-2 image dataset,compared with the extraction accuracy by using these two datasets alone and without the unlabeled images,respectively 1.0 percentage points,4.9 percentage points,and 8.1 percentage points.Under the realistic background of abundant cropland remote sensing data and insufficient corresponding labeled information.The semisupervised model learned the feature information in the images of cultivated land in the proposed extraction area and the training set area,and improved the extraction accuracy of heterogeneous cultivated land.A method that maximized the learning of heterogeneous cropland unlabeled images and a minimum amount of labeled data using a semi-supervised learning model for the task of wide-area cropland extraction was proposed,and the extraction results were effective.