用条件随机场提升卫星图像分割清晰度
Post Processing of image segmentation using Conditional Random Fields
- 采用多种条件随机场优化分割结果
- 在低质卫星图与高质航拍图上验证效果
- 揭示不同CRF在实际场景中的优劣
由于卫星图像特征质量较低，图像分割结果通常不够清晰。本研究旨在寻找适合的条件随机场（Conditional Random Field, CRF）以提升分割图像的清晰度。我们尝试了多种类型的CRF，分析其适用性与局限性。在包含低质量卫星影像和高质量航空照片的两个数据集上进行了评估。通过对比不同CRF在两类图像上的表现，揭示了各方法的潜在优势与不足，为实际应用提供了参考。
原文摘要 · Abstract (English)
The output of image the segmentation process is usually not very clear due to low quality features of Satellite images. The purpose of this study is to find a suitable Conditional Random Field (CRF) to achieve better clarity in a segmented image. We started with different types of CRFs and studied them as to why they are or are not suitable for our purpose. We evaluated our approach on two different datasets - Satellite imagery having low quality features and high quality Aerial photographs. During the study we experimented with various CRFs to find which CRF gives the best results on images and compared our results on these datasets to show the pitfalls and potentials of different approaches.
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