Remote Sensing Scene Image Classification Based on mmsCNN–HMM with Stacking Ensemble Model

Remote Sensing Image Classification

An ensemble learning method for scene classification based on Hidden Markov Model image representation

Low level images representation in feature space performs poorly for classification with high accuracy since this level of representation is not able to project images into the discriminative feature space. In this work, we propose an efficient image representation model for classification. First we apply Hidden Markov Model (HMM) on ordered grids represented by different type of image descript...

Remote Sensing Image Classification Based on Gray System Theory

The intelligence and automation of image processing and analysis is a bottle problem for photogrammetry and remote sensing. Artificial neural networks is a new solver which imitates brain and gray system theory is a new tool which handles undetermined problem. This paper describe how to combine artificial neural networks with gray system theory to realize classification of remote sensing image ...

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised ...

Research of Forest Classification Based on Remote Sensing Image

The forest plays an important role in regulating climate and improving ecological carrying capacity. In view of heterogeneous mixed young afforestation, the object-based classification on rules method is used to identify the types of planted forest, combined with spectrum, texture and shape characteristics information. ESP tool was applied to obtain the best segmentation scale and the rule set ...