基于SAE-1DCNN特征选择方法的高光谱降维

IF 2.3 Q2 REMOTE SENSING Applied Geomatics Pub Date : 2023-11-13 DOI:10.1007/s12518-023-00535-6
Mario Ernesto Jijón-Palma, Caisse Amisse, Jorge Antonio Silva Centeno
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引用次数: 0

摘要

高光谱遥感能够对目标表面进行详细的光谱描述,但由于狭窄的连续光谱带高度相关,它也引入了高冗余。这有两个后果:休斯现象和由于数据量增加而增加的处理工作量。在本研究中,提出了一种将堆叠自编码器与卷积神经网络相结合的模型来解决基于特征选择方法的频谱冗余问题。特征选择与特征提取相比有很大的优势,它不需要对原始数据进行任何转换,避免了在转换过程中信息的丢失。该模型使用卷积堆叠自编码器学习将输入数据表示为优化的高级特征集。一旦SAE学会了表示最优特征,解码器部分就会被替换为规则的神经元层,以减少冗余。该模型的优点是能够在保留原始波段有意义信息的基础上,自动选择和提取具有代表性的特征,从而提高高光谱图像的主题分类能力。利用机载可见/红外成像光谱仪(AVIRIS)传感器的两个高光谱数据集(Indian Pines和Salinas)进行了多次实验,以评估所提出方法的性能。分析结果表明,与其他降维特征选择方法相比,该模型具有较高的精度和有效性。因此,该模型可以用作降维的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Hyperspectral dimensionality reduction based on SAE-1DCNN feature selection approach

Hyperspectral remote sensing enables a detailed spectral description of the object’s surface, but it also introduces high redundancy because the narrow contiguous spectral bands are highly correlated. This has two consequences, the Hughes phenomenon and increased processing effort due to the amount of data. In the present study, it is introduced a model that integrates stacked-autoencoders and convolutional neural networks to solve the spectral redundancy problem based on the feature selection approach. Feature selection has a great advantage over feature extraction in that it does not perform any transformation on the original data and avoids the loss of information in such a transformation. The proposed model used a convolutional stacked-autoencoder to learn to represent the input data into an optimized set of high-level features. Once the SAE is learned to represent the optimal features, the decoder part is replaced with regular layers of neurons for reduce redundancy. The advantage of the proposed model is that it allows the automatic selection and extraction of representative features from a dataset preserving the meaningful information of the original bands to improve the thematic classification of hyperspectral images. Several experiments were performed using two hyperspectral data sets (Indian Pines and Salinas) belonging to the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor to evaluate the performance of the proposed method. The analysis of the results showed precision and effectiveness in the proposed model when compared with other feature selection approaches for dimensionality reduction. This model can therefore be used as an alternative for dimensionality reduction.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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