Terrain Feature Extraction and Classification using Contact Sensor Data

Feature Extraction for Terrain Classification with Crawling Robots

In this paper, we address the problem of terrain classification using a technically blind hexapod walking robot. The proposed approach is built on top of the existing method based on analysis of the feedback from the robot’s actuators and the desired trajectory. The formed method uses features for the Support Vector Machine classification method that assumes a regular time-invariant gait to con...

Crop Classification with Hyperspectral Data of the Hymap Sensor Using Different Feature Extraction Techniques

A method is presented to extract a subset of individual bands from a hyperspectral dataset. The method seeks to maximize the information content in a given subset by analyzing the covariances between individual bands of the original dataset. Subsequently, the method is used to reduce the dimensionality of hyperspectral image data prior to maximum likelihood classification. The classification re...

Hyperspectral Data Classification Using Nonparametric Weighted Feature Extraction

In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features de...

Classification of Hyperspectral Data using Best-bases Feature Extraction Algorithms

Polyline Feature Extraction for Land Cover Classification using Hyperspectral Data

Prediction of landcover types from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to recent advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in ∼200 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. This unprecedented spectral resolution ...