Can I read A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images on EtoBox?

A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images by Chen Zheng; Chen Hu; Yuncheng Chen; Jingying Li is a Engineering article available to read on EtoBox.

What is A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images about?

Convolutional neural network (CNN) has been widely used in semantic segmentation for remote sensing images, and it has achieved great success. Due to the diversity of the spatial distribution of terrestrial objects in remote sensing images, it is difficult to effectively learn general geographical laws and apply them to a specific image. To introduce geographical knowledge into the CNN model more effectively, a self-learningupdate CNN model (SLU-CNN) is proposed in this letter. It learns the representation of specific spatial dependence among different objects according to the CNN result, and then incorporates it with the CNN result to make semantic inference available. The proposed method mainly involves two modules. First, geographical objects generated from the CNN result are used as inference units. Second, the spatial dependence between inference units is learned to build a specific adaptive geographical relationship. And then, it is embedded as an adaptive penalty term into an object-based Markov random field model (OMRF) to achieve the collaboration between the CNN result and the semantic inference. Our method provides a general data-knowledge dualdriven framework for the de

Who reads A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images?

Author

Chen Zheng; Chen Hu; Yuncheng Chen; Jingying Li

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)