面向高光譜圖像分類的半監(jiān)督Laplace鑒別嵌入
doi: 10.11999/JEIT140600
基金項目:
國家自然科學基金(61101168, 41371338)，中國博士后科學基金(2012M511906, 2013T60837)，重慶市基礎(chǔ)與前沿研究計劃項目(cstc2013jcyjA40005)，重慶市國土房管局科技計劃項目(CQGT-KJ-2012028)和博士后科研計劃項目(2012M511906, 2013T60837, XM2012001)資助課題
Semi-supervised Laplace Discriminant Embedding for Hyperspectral Image Classification
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摘要: 為有效提取出高光譜遙感圖像數(shù)據(jù)的鑒別特征，該文闡述一種融合標記樣本中鑒別信息和無標記樣本中局部結(jié)構(gòu)信息的半監(jiān)督Laplace鑒別嵌入(SSLDE)算法。該算法利用標記樣本的類別信息來保持樣本集的可分性，并通過構(gòu)建標記樣本和無標記樣本的Laplace矩陣來發(fā)現(xiàn)樣本集中局部流形結(jié)構(gòu)，實現(xiàn)半監(jiān)督的流形鑒別。在KSC 和Urban數(shù)據(jù)集上的實驗結(jié)果說明：該算法具有更高的分類精度，可以有效地提取出鑒別特征信息。在總體分類精度上，該算法比半監(jiān)督最大邊界準則(SSMMC)算法提升了6.3%~7.4%，比半監(jiān)督流形保持嵌入(SSSMPE)算法提升了1.6%~4.4%。
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關(guān)鍵詞:
- 圖像處理 /
- 高光譜遙感圖像 /
- 鑒別特征 /
- Laplace矩陣 /
- 半監(jiān)督Laplace鑒別嵌入
Abstract: In order to extract effectively the discriminant characteristics of hyperspectral remote sensing image data, this paper presents a Semi-Supervised Laplace Discriminant Embedding (SSLDE) algorithm based on the discriminant information of labeled samples and the local structural information of unlabeled samples. The proposed algorithm makes use of the class information of labeled samples to maintain the separability of sample set, and discovers the local manifold structure in sample set by constructing Laplace matrix of labeled and unlabeled samples, which can achieve semi-supervised manifold discriminant. The experimental results on KSC and Urban database show that the algorithm has higher classification accuracy and can effectively extract the information of discriminant characteristics. In the overall classification accuracy, this algorithm is improved by 6.3%~7.4% compared with Semi-Supervised Maximum Margin Criterion (SSMMC) algorithm and increased by 1.6%~4.4% compared with Semi-Supervised Sub-Manifold Preserving Embedding (SSSMPE) algorithm. -
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