Mask R-CNN-based intelligent identification of sparse woods from remote sensing images
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摘要: 针对遥感影像疏林地提取方法少且精度不高,缺乏智能识别的数据集情况,提出了一套遥感影像疏林地智能识别方法。分别使用QGIS插件和Python语言对该方法进行实现,完成了数据集制作的环节,为模型训练提供了数据支撑; 通过特征提取生成特征图,在特征图中提取感兴趣区域(region of interest,ROI),通过池化操作(ROI align)对这些感兴趣区域进行过滤操作,减少因疏林地图像感兴趣区域过多而造成的内存消耗。实验表明,该方法可快速进行数据集制作,有效辅助遥感影像中疏林地的识别,使用基于Mask R-CNN的遥感影像疏林地智能识别方法对疏林地目标检测的均值平均准确率可以达到0.92。
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关键词:
- 遥感影像 /
- 疏林地 /
- 深度学习 /
- Mask R-CNN /
- QGIS
Abstract: There are only a few low-accuracy methods available for the feature extraction of sparse woods from remote sensing images. Moreover, there is a lack of datasets for intelligent identification. This study proposed a method for intelligent information identification of sparse woods from remote sensing images. First, a dataset was created using QGIS and Python separately to provide data support for model training. Then, feature maps were generated through feature extraction, and then regions of interest (ROIs) were extracted from the feature maps. Subsequently, these ROIs were filtered through pooling operations (ROI align) to reduce the memory consumption caused by too many ROIs in the images. Experiments show that the method proposed in this study can create datasets quickly and facilitate the identification of sparse woods from remote sensing images. Moreover, the Mask R-CNN-based intelligent identification has a target detection mean average precision (MAP) of up to 0.92.-
Key words:
- remote sensing image /
- sparse woods /
- deep learning /
- Mask R-CNN /
- QGIS
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