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Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
[article]
2017
arXiv
pre-print
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation and classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance. In this paper, we propose a method to segment
arXiv:1711.04483v2
fatcat:vensfbnbjfgh3hnxdakobqb2l4