A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Abstract

The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier.

A Efficient Solution for Classification of Crops using Hyper Spectral Satellite Images

“…This paper also investigates the different variation on the weed identification. [17] Proposes the algorithm for choosing the feature that has the highest preference separatibility through using the RF method that can differentiate the categories, which are easily distinguishable and choose the feature from the complexed categories through WEA (Weight Entropy Algorithm). This particular framework is made up of five different part and they are 1) RSS (Random Sample Selection) 2) Probabilistic RF classification depending on the given number of votes.…”

Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling

“…Handcrafted feature representations were previously used by the following traditional pedestrian detectors: Haar [7], [8], scale invariant feature transform (SIFT) [9], [10], histogram of oriented gradient (HOG) [11]- [13], and local binary pattern (LBP) [7] [14]. To perform pedestrian classification, these feature representations are combined with classifiers such as support vector machine (SVM) [15], [16] boosted forests [10], and AdaBoost [17]. However, detection difficulties arise when the pedestrian and the attached instances are partially obscured.…”