Hyperspectral Remote Sensing Image Classification Based on Rotation Forest

Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest

Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since...

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 ...

A Survey of Hyperspectral Image Classification in Remote Sensing

Hyperspectral image processing has been a very dynamic area in remote sensing and other applications in recent years. Hyperspectral images provide ample spectral information to identify and distinguish spectrally similar materials for more accurate and detailed information extraction. Wide range of advanced classification techniques are available based on spectral information and spatial inform...

An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on Spectral Data Mining

© 2012 Wen and Yang, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on ...

Remote Sensing Image Classification