Mario Ernesto Jijón-Palma, Caisse Amisse, Jorge Antonio Silva Centeno
{"title":"基于SAE-1DCNN特征选择方法的高光谱降维","authors":"Mario Ernesto Jijón-Palma, Caisse Amisse, Jorge Antonio Silva Centeno","doi":"10.1007/s12518-023-00535-6","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperspectral dimensionality reduction based on SAE-1DCNN feature selection approach\",\"authors\":\"Mario Ernesto Jijón-Palma, Caisse Amisse, Jorge Antonio Silva Centeno\",\"doi\":\"10.1007/s12518-023-00535-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":46286,\"journal\":{\"name\":\"Applied Geomatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geomatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12518-023-00535-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-023-00535-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
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.
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