Post Processing of image segmentation using Conditional Random Fields
This work addresses image segmentation clarity for satellite imagery, but it is incremental as it applies existing CRF methods to new data without introducing novel techniques.
The study tackled the problem of unclear image segmentation outputs from low-quality satellite images by evaluating various Conditional Random Fields (CRFs) to improve clarity, achieving better results on both low-quality satellite and high-quality aerial datasets.
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.