本文選題：高分辨率遙感圖像分類 + KNNSVM算法　； 參考：《中南民族大學(xué)學(xué)報(bào)(自然科學(xué)版)》2016年01期

【摘要】：為了解決局部支持向量機(jī)算法KNNSVM存在的分類時(shí)間過長(zhǎng)不利于具有海量數(shù)據(jù)量的高分辨率遙感圖像分類的不足,提高KNNSVM的算法表現(xiàn),提出了改進(jìn)的基于不確定性的BKNNSVM算法.該算法利用二項(xiàng)式分布的共軛先驗(yàn)分布Beta分布根據(jù)近鄰的分布情況推導(dǎo)該未標(biāo)記樣本屬于正類或負(fù)類的概率大小,從而計(jì)算每一個(gè)未標(biāo)記樣本在類屬性上的不確定性大小.再通過設(shè)置不確定性閾值的大小,對(duì)不確定性低于閾值的未標(biāo)記樣本直接采用KNN進(jìn)行分類,而對(duì)高于閾值的樣本利用其近鄰建立局部支持向量機(jī)分類器進(jìn)行分類.對(duì)高分辨率圖像分類的實(shí)驗(yàn)結(jié)果表明:合適的閾值能夠有效降低原始KNNSVM算法的時(shí)間開銷,同時(shí)能保持KNNSVM分類精度高的特點(diǎn).
[Abstract]:In order to solve the problem that the classification time of local support vector machine (KNNSVM) algorithm is too long which is not conducive to the classification of high-resolution remote sensing images with large amount of data, and to improve the performance of KNNSVM algorithm, an improved BKNNSVM algorithm based on uncertainty is proposed.By using the conjugate priori Beta distribution of binomial distribution, the probability of the unlabeled sample belonging to a positive or negative class is derived according to the distribution of the nearest neighbor, so as to calculate the uncertainty of each unlabeled sample in class attributes.By setting the uncertainty threshold, the unlabeled samples with uncertainty below the threshold are classified directly by KNN, and the samples above the threshold are classified by local support vector machine (SVM) classifier using their nearest neighbors.The experimental results for high-resolution image classification show that the appropriate threshold can effectively reduce the time cost of the original KNNSVM algorithm and maintain the high accuracy of KNNSVM classification.
【作者單位】： 中南民族大學(xué)電信學(xué)院;中國(guó)地質(zhì)大學(xué)地球物理與空間信息學(xué)院;
【基金】：湖北省自然科學(xué)基金資助項(xiàng)目(PBZY14019)
【分類號(hào)】：TP751
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本文編號(hào)：1735550