Remote Sensing Image Analysis via a Texture Classification Neural Network
Abstract
In this work we apply a texture classification network to remote sensing im(cid:173) age analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain .
Cite
Text
Greenspan and Goodman. "Remote Sensing Image Analysis via a Texture Classification Neural Network." Neural Information Processing Systems, 1992.Markdown
[Greenspan and Goodman. "Remote Sensing Image Analysis via a Texture Classification Neural Network." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/greenspan1992neurips-remote/)BibTeX
@inproceedings{greenspan1992neurips-remote,
title = {{Remote Sensing Image Analysis via a Texture Classification Neural Network}},
author = {Greenspan, Hayit K. and Goodman, Rodney},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {425-432},
url = {https://mlanthology.org/neurips/1992/greenspan1992neurips-remote/}
}