Hyperspectral Image Classification With Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random Fields

Li, Fan; Xu, Linlin; Siva, Parthipan; Wong, Alexander; Clausi, David A.

doi:10.1109/jstars.2015.2414816

Cited by 55 publications

(28 citation statements)

References 51 publications

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“…In order to evaluate the performance of the proposed classification techniques, some methods including support vector machine(SVMs), DT, RotBoost [46,47], DT with KOPLS (DT-KOPLS), and RoF-PCA were implemented for comparison. The reason why we select SVMs and DT in comparison to the proposed methods is that they are two of the leading classification techniques of hyperspectral data.…”

Section: Results Of the Aviris Indian Pines Imagementioning

confidence: 99%

Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Chen

Xia

et al. 2016

Remote Sensing

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Kernel-based methods and ensemble learning are two important paradigms for the classification of hyperspectral remote sensing images. However, they were developed in parallel with different principles. In this paper, we aim to combine the advantages of kernel and ensemble methods by proposing a kernel supervised ensemble classification method. In particular, the proposed method, namely RoF-KOPLS, combines the merits of ensemble feature learning (i.e., Rotation Forest (RoF)) and kernel supervised learning (i.e., Kernel Orthonormalized Partial Least Square (KOPLS)). In particular, the feature space is randomly split into K disjoint subspace and KOPLS is applied to each subspace to produce the new features set for the training of decision tree classifier. The final classification result is assigned to the corresponding class by the majority voting rule. Experimental results on two hyperspectral airborne images demonstrated that RoF-KOPLS with radial basis function (RBF) kernel yields the best classification accuracies due to the ability of improving the accuracies of base classifiers and the diversity within the ensemble, especially for the very limited training set. Furthermore, our proposed method is insensitive to the number of subsets.

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Section: Results Of the Aviris Indian Pines Imagementioning

confidence: 99%

Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Chen

Xia

et al. 2016

Remote Sensing

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“…The conventional CRF considering local spatial information along with spectral information has a proven capability to classify Landsat images, therefore, CRFs have been widely used in the remote sensing (Salmon et al, 2015;Li et al, 2015;Xu et al, 2017). The fully-connected CRF (FCRF) addresses the correlation effect in the global image scale is better than the conventional CRF that considers the correlation effect in a local area (Krä henbühl et al, 2011).…”

Section: Stochastic Fully-connected Conditional Random Fieldmentioning

confidence: 99%

Waterbodies Extraction From Landsat8-Oli Imagery Using Awater Indexs-Guied Stochastic Fully-Connected Conditional Random Field Model and the Support Vector Machine

Xian-bin

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method's performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

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“…One of the applications of remote seining is hyperspectral imaging. Hyperspectral imaging has been extensively used in many practical applications such as monitoring of land changes, urban development, environmental and mineral exploration [1].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction

Alqarni

Khan

Hussain

et al. 2019

Optik

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Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of loosing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE) based BR method is proposed. Our aim is to find an optimal mapping and construct a lowerdimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original hyperspectral data into smaller regions in spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for both semi-supervised and unsupervised tasks, i.e. classification and clustering. Our experiments on publicly available hyperspectral datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.

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Hyperspectral Image Classification With Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random Fields

Cited by 55 publications

References 51 publications

Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Waterbodies Extraction From Landsat8-Oli Imagery Using Awater Indexs-Guied Stochastic Fully-Connected Conditional Random Field Model and the Support Vector Machine

Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction

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