Post Processing of image segmentation using Conditional Random Fields

2025/10/10 by Aashish Dhawan, Dhawan, Aashish, Pankaj Bodani +3
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.2510.09833

openalex created_date 2020/02/24 · openalex publication_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
• W-Net: A Deep Model for Fully Unsupervised Image Segmentation
• Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Cited by

• RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation

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