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邢长达，汪美玲，徐雍倡，王志胜.2024.具有分类器机制的高光谱图像特征提取方法.遥感学报，28（2）： 511-527
Xing C D，Wang M L，Xu Y C and Wang Z S.2024. Classifier mechanism embedded feature-extraction method for hyperspectral images. National Remote Sensing Bulletin， 28（2）：511-527
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%。
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.
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
hyperspectral image classificationfeature extractionclassifier mechanismsparse representationdeep autoencoder network
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