Active Learning Algorithms for the Classification of Hyperspectral Sea Ice Images

Yanling Han,Yun Zhang,Qiming Gu,Zhonghua Hong,Jing Ren,Long Zhang,Wanting Meng

doi:10.1155/2015/124601

Abstract

Sea ice is one of the most critical marine disasters, especially in the polar and high latitude regions. Hyperspectral image is suitable for monitoring the sea ice, which contains continuous spectrum information and has better ability of target recognition. The principal bottleneck for the classification of hyperspectral image is a large number of labeled training samples required. However, the collection of labeled samples is time consuming and costly. In order to solve this problem, we apply the active learning (AL) algorithm to hyperspectral sea ice detection which can select the most informative samples. Moreover, we propose a novel investigated AL algorithm based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is based on the difference between the probabilities of the two classes having the highest estimated probabilities, while the diversity criterion is based on a kernelk-means clustering technology. In the experiments of Baffin Bay in northwest Greenland on April 12, 2014, our proposed AL algorithm achieves the highest classification accuracy of 89.327% compared with other AL algorithms and random sampling, while achieving the same classification accuracy, the proposed AL algorithm needs less labeling cost.

Highlights

As a member of the global marine and atmospheric system, sea ice with the high albedo has impacted the marine, power between the atmospheres, and heart and material exchange
Results are presented as learning rate curves, which show the relation between the average overall classification accuracies and the active learning rounds used to train the support vector machine (SVM) classifier
We can find that performing the clustering in the kernel space can improve the classification accuracy compared with the standard k-means clustering

Summary

Introduction

As a member of the global marine and atmospheric system, sea ice with the high albedo has impacted the marine, power between the atmospheres, and heart and material exchange. Expect for the influence on marine hydrology, atmospheric circulation, and ecosystems, sea ice has a great threat to the shipping and the facilities of marine resource development and has become one of the most prominent oceanic disasters in the polar and high latitude regions. The main research areas of sea ice detection with the remote sensing are in the polar and high latitude regions. The countries carrying out the relevant research include America, Canada, Norway, Australia, and German. These researches mainly aimed at the moderate-resolution remote sensing detections, such as airborne remote sensing, moderate-resolution imaging spectrometer (MODIS) [2], and synthetic aperture radar (SAR). The researches on sea ice detection with hyperspectral technology are rarely involved by far

Methods

Results

Conclusion