首页|具有分类器机制的高光谱图像特征提取方法

具有分类器机制的高光谱图像特征提取方法

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高光谱图像分类是图像解译任务的重要技术之一,已经在遥感观测、智慧医疗等诸多领域得到广泛的应用.本质上,高光谱图像分类由特征提取与基于分类器的标签预测这两阶段操作组成.现有分类方法在特征提取时,大多不考虑分类器的影响,会导致提取的特征与所用分类器之间的兼容性较差,难免出现预测结果差的情况.针对此问题,本文提出具有分类器机制的高光谱图像特征提取方法,保证特征提取与分类器之间的兼容性,使特征能更易于被分类器准确计算,改善分类预测结果.本文给出了两种具有分类器机制的高光谱图像特征提取模型的形式:(1)以稀疏表示和支持向量机为例,将支持向量机特性集成到稀疏表示形式中,建立了能够与支持向量机分类器相兼容的SRS特征提取模型;(2)以深度自编码网络与softmax函数为例,将softmax分类器特性嵌入到深度自编码网络中,构建能与softmax分类器相兼容的DAES特征提取模型.为获得SRS和DAES模型的解,本文还给出了对应的求解策略与优化过程.在遥感高光谱图像和医学高光谱图像数据上开展实验验证,结果表明,本文SRS和DAES算法具有明显的有效性和优越性,在高光谱图像分类指标OA(Overall Accuracy)、AA(Average Accuracy)、Kappa上分别提升约5.03%、5.13%、7.30%.
Classifier mechanism embedded feature-extraction method for hyperspectral images
As an important technique in image interpretation,hyperspectral image(HSI)classification is extensively used in many fields,such as remote-sensing observation and intelligent medical service.HSI classification may comprise label prediction based on feature extraction and based on classifiers.Although deep learning can directly obtain the classification results by one step,which is achieved by the end-to-end network structure from data input to classification result output,they are actually viewed as a direct cascade of both feature extraction based on deep networks(such as deep autoencoder and convolutional neural network)and classifiers(such as softmax and logistic regression).Most current classification approaches do not consider the influence of classifiers on feature extraction,which may cause the incompatibility between the extracted features and the used classifier.This incompatibility is reflected in the poor matching relationship between the classifier model and its input feature data,leading to poor prediction results.Method To remedy such deficiency,this paper presents a novel kind of HSI feature-extraction methods embedded by the classifier mechanism,which can ensure the compatibility between feature extraction and the used classifier.Thus,the features can be more easily calculated by classifier accurately,and classification prediction results can be improved.Two specific forms are given in this paper.1)The sparse representation(SR)feature-extraction model compatible with Support Vector Machine(SVM)classifier is built,which embeds the SVM property into the SR.2)The deep autoencoder(DAE)feature-extraction model compatible with softmax classifier is constructed,which integrates the softmax function into DAE network.We also provide the optimization strategy to obtain the optimal solutions of the SR and DAE models.Results The proposed SR and DAE models are experimentally evaluated on the remote-sensing HSI data and medical HSI data.The experiments consist of parameter analysis,algorithm comparison,ablation study,and convergence analysis.According to the parameter analysis,we validate that the values of important parameters have obvious impact on the performance of our methods and successfully select the best values of these parameters.As suggested by the algorithm comparison,the proposed methods achieve better classification performance than some state-of-the-art approaches,which have obvious effectiveness and superiority.The overall accuracy,average accuracy,and Kappa indices in the HSI classification task are,on average,higher by 5.03%,5.13%,and 7.30%,respectively.An ablation study is conducted to demonstrate the effectiveness of the compatibility between feature extraction and the bedded classifiers for the performance improvement of HSI classification.Convergence analysis indicates that the designed optimization-solution strategy can meet the application requirements of reliability and rapidity.Conclusion The proposed SR and DAE methods realize good compatibility between feature extraction and classifiers.Accordingly,the extracted features can be better calculated by classifiers,and more competitive classification performance can be achieved.

hyperspectral image classificationfeature extractionclassifier mechanismsparse representationdeep autoencoder network

邢长达、汪美玲、徐雍倡、王志胜

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中国矿业大学信息与控制工程学院,徐州 221116

南京航空航天大学计算机科学与技术学院,南京 211106

南京航空航天大学 自动化学院,南京 211106

高光谱图像分类 特征提取 分类器机制 稀疏表示 深度自编码网络

国家自然科学基金国家自然科学基金中国博士后科学基金中央引导地方科技发展专项

62101247621061042022T1503202021Szvup063

2024

遥感学报
中国地理学会环境遥感分会 中国科学院遥感应用研究所

遥感学报

CSTPCD北大核心
影响因子:2.921
ISSN:1007-4619
年,卷(期):2024.28(2)
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