Deep learning ensemble method for classification of satellite hyperspectral images

Iyer, Praveen; Sriram, Aditya; Lal, Shyam

doi:10.1016/j.rsase.2021.100580

Cited by 14 publications

(13 citation statements)

References 27 publications

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Section: Ensemble Methodsmentioning

confidence: 99%

“…Deep neural network techniques in HSI semantic segmentation include [38] and [39]. The authors in [39] do a boosting ensemble method called Deep CNN Ensemble where they take the top performing models, HybridSN and ResNet, for their submodels. However, the boosting method increases the running time exponentially because of training multiple models on the same pixels.…”

Section: Ensemble Methodsmentioning

confidence: 99%

“…Our single U-Net and CEU-Net networks outperform the baseline HybridSN in almost all datasets used except Botswana. For ensemble methods, our CEU-Net outperforms the ensemble baselines, EECNN [38], Deep CNN Ensemble [39], and TCNN-E-ILS [40], in all datasets except for TCNN-E-ILS for KSC, despite having CEU-Net running with smaller patch sizes.…”

Section: Semantic Segmentationmentioning

confidence: 96%

“…CPC was used with a patch sizes of 5 x 5, 10 x 10, and 15 x 15. For our ensemble network baselines, the results from [38][39][40] vary in feature reduction method and patch sizes compared to the single network baselines. In addition, we pick the best metrics from the ensemble works with the best ensemble size T. Therefore, we place T = x next to each method to represent the ensemble size used in the corresponding experiments.…”

Section: Semantic Segmentationmentioning

confidence: 99%

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CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering

Soucy

Sekeh

2023

J Big Data

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Most semantic segmentation approaches of big data hyperspectral images use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use patching to incorporate the rich spatial neighborhood information in images and exploit the simplicity and segmentability of the most common datasets. In contrast, most landmasses in the world consist of overlapping and diffused classes, making neighborhood information weaker than what is seen in common datasets. To combat this common issue and generalize the segmentation models to more complex and diverse hyperspectral datasets, in this work, we propose a novel flagship model: Clustering Ensemble U-Net. Our model uses the ensemble method to combine spectral information extracted from convolutional neural network training on a cluster of landscape pixels. Our model outperforms existing state-of-the-art hyperspectral semantic segmentation methods and gets competitive performance with and without patching when compared to baseline models. We highlight our model’s high performance across six popular hyperspectral datasets including Kennedy Space Center, Houston, and Indian Pines, then compare them to current top-performing models.

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Section: Ensemble Methodsmentioning

confidence: 99%

Section: Ensemble Methodsmentioning

confidence: 99%

Section: Semantic Segmentationmentioning

confidence: 96%

Section: Semantic Segmentationmentioning

confidence: 99%

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CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering

Soucy

Sekeh

2023

J Big Data

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“…Many existing works use these aggregate attacks now to test performance, but still, test one aggregate attack at a time. Ensemble Methods: In HSI semantic segmentation, the more successful ensemble models have focused on using individual networks that work in parallel on sub-sets of the data for better overall performance [16,17,18,19]. The ensemble EECNN method [16] applies a random sampling technique on the feature space to obtain the data subsets for each submodel.…”

Section: Related Workmentioning

confidence: 99%

Improving Hyperspectral Adversarial Robustness using Ensemble Networks in the Presences of Multiple Attacks

Soucy¹,

Sekeh²

2022

Preprint

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Semantic segmentation of hyperspectral images (HSI) has seen great strides in recent years by incorporating knowledge from deep learning RGB classification models. Similar to their classification counterparts, semantic segmentation models are vulnerable to adversarial examples and need adversarial training to counteract them. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease the performance compared to networks trained individually on each attack. To combat this issue we propose an Adversarial Discriminator Ensemble Network (ADE-Net) which focuses on attack type detection and adversarial robustness under a unified model to preserve per data-type weight optimally while robustifiying the overall network. In the proposed method, a discriminator network is used to separate data by attack type into their specific attack-expert ensemble network. Our approach allows for the presence of multiple attacks mixed together while also labeling attack types during testing. We experimentally show that ADE-Net outperforms the baseline, which is a single network adversarially trained under a mix of multiple attacks, for HSI Indian Pines, Kennedy Space, and Houston datasets.

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Optimized weighted local kernel features for hyperspectral image classification

Beirami

Mokhtarzade

2022

Multimed Tools Appl

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Deep learning ensemble method for classification of satellite hyperspectral images

Cited by 14 publications

References 27 publications

CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering

CEU-Net: ensemble semantic segmentation of hyperspectral images using clustering

Improving Hyperspectral Adversarial Robustness using Ensemble Networks in the Presences of Multiple Attacks

Optimized weighted local kernel features for hyperspectral image classification

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