Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions

To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer method...

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Autores principales:	Wei Hu, Xiyuan Kong, Liang Xie, Huijiong Yan, Wei Qin, Xiangyi Meng, Ye Yan, Erwei Yin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	heterogeneous feature transfer classification of remote sensing images transfer learning negative transfer Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/782560f7745a484095a819da22719c99
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id	oai:doaj.org-article:782560f7745a484095a819da22719c99
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spelling	oai:doaj.org-article:782560f7745a484095a819da22719c992021-11-25T18:57:31ZJoint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions10.3390/s212275681424-8220https://doaj.org/article/782560f7745a484095a819da22719c992021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7568https://doaj.org/toc/1424-8220To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.Wei HuXiyuan KongLiang XieHuijiong YanWei QinXiangyi MengYe YanErwei YinMDPI AGarticleheterogeneous feature transferclassification of remote sensing imagestransfer learningnegative transferChemical technologyTP1-1185ENSensors, Vol 21, Iss 7568, p 7568 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heterogeneous feature transfer classification of remote sensing images transfer learning negative transfer Chemical technology TP1-1185
spellingShingle	heterogeneous feature transfer classification of remote sensing images transfer learning negative transfer Chemical technology TP1-1185 Wei Hu Xiyuan Kong Liang Xie Huijiong Yan Wei Qin Xiangyi Meng Ye Yan Erwei Yin Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
description	To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.
format	article
author	Wei Hu Xiyuan Kong Liang Xie Huijiong Yan Wei Qin Xiangyi Meng Ye Yan Erwei Yin
author_facet	Wei Hu Xiyuan Kong Liang Xie Huijiong Yan Wei Qin Xiangyi Meng Ye Yan Erwei Yin
author_sort	Wei Hu
title	Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
title_short	Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
title_full	Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
title_fullStr	Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
title_full_unstemmed	Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions
title_sort	joint feature-space and sample-space based heterogeneous feature transfer method for object recognition using remote sensing images with different spatial resolutions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/782560f7745a484095a819da22719c99
work_keys_str_mv	AT weihu jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT xiyuankong jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT liangxie jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT huijiongyan jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT weiqin jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT xiangyimeng jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT yeyan jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions AT erweiyin jointfeaturespaceandsamplespacebasedheterogeneousfeaturetransfermethodforobjectrecognitionusingremotesensingimageswithdifferentspatialresolutions
_version_	1718410470067535872

Joint Feature-Space and Sample-Space Based Heterogeneous Feature Transfer Method for Object Recognition Using Remote Sensing Images with Different Spatial Resolutions

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