Cloud Implementation of Multinomial Logistic Regression for UAV Hyperspectral Images

Haut, Juan M.; Paoletti, Mercedes E.

doi:10.1109/jmass.2020.3019669

Cited by 28 publications

(12 citation statements)

References 70 publications

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“…Class RF [75] MLR [321] SVM [322] MLP [69] RNN [70] LSTM [323] GRU [324] CNN-1D [218] CNN-2D [325] CNN-3D [326] HybridSN [213] MorphCNN [196 CNN-1D, CNN-2D, CNN-3D, and MorphCNN have been considered to compare the experimental results. Some of the representative works for each above are as follows; Cloud Implementation of Logistic Regression for HSIC [321], [327], [328] 10-crossvalidation process to compute the overall, average and kappa (κ) accuracy for comparison purposes. For instance, let us assume the case of Pavia University results, for this particular case, the work [196] has the highest average, overall and kappa (κ) accuracies which are 95.51%, 93.95%, and 93.95% respectively in comparison with the average, overall and kappa (κ) accuracies for other comparative works; 92.55%, 89.94%, 89.9% for [325]…”

Section: B Experimental Results On Disjoint Train/test Samplesmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

216

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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the stateof-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that

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Section: B Experimental Results On Disjoint Train/test Samplesmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

216

View full text Add to dashboard Cite

show abstract

“…These methods are available on [5] 2 . In particular, the methods considered include: multinomial logistic regression (MLR) [35], SVM with radial basis function [25], gated recurrent unit (GRU) [36], long short term memory (LSTM) [37], CNN-1D [38], CNN-2D [5] and CNN-3D [39], [40]. Moreover, to feed spatial-based models, a spatial neighborhood of size 11×11 has been considered to create patches for all the HSI datasets.…”

Section: B Experimental Settingsmentioning

confidence: 99%

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Roy¹,

Mondal

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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Convolutional neural networks (CNNs) have become quite popular for solving many different tasks in remote sensing data processing. The convolution is a linear operation which extracts features from the input data. However, nonlinear operations are able to better characterize the internal relationships and hidden patterns within complex remote sensing data, such as hyperspectral images (HSIs). Morphological operations are powerful nonlinear transformations for feature extraction that preserve the essential characteristics of the image, such as borders, shape and structural information. In this paper, a new end-to-end morphological deep learning framework (called MorphConvHyperNet) is introduced. The proposed approach efficiently models nonlinear information during the training process of HSI classification. Specifically our method includes spectral and spatial morphological blocks to extract relevant features from the HSI input data. These morphological blocks consist of two basic 2D morphological operators (erosion and dilation) in the respective layers, followed by a weighted combination of the feature maps. Both layers can successfully encode the nonlinear information related to shape and size, playing an important role in classification performance. Our experimental results, obtained on five widely used HSIs, reveal that our newly proposed MorphConvHyperNet offers comparable (and even superior) performance when compared to traditional 2D and 3D

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“…Meanwhile, we list the prediction accuracy of each category in comparisons. The compared methods include classical methods, such as MLR [17], SVM [35], and LSTM [36], and recent proposed deep learning methods, such as ResNet [18], C-Net [26] M-Net [19], E-FCN [40], SSRN [49], and ASK-Net [38]. The comparisons on IndianPines, PaviaU, and KSC are shown in Table 10, 11, and 12, respectively.…”

Section: Comparisons With Existing Methods With Disjoint Deviation St...mentioning

confidence: 99%

Boosting Hyperspectral Image Classification with Dual Hierarchical Learning

Wang

Ben

Hao

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Hyperspectral image (HSI) classification aims at predicting the pixel-wise labels in an image, where there are only a few labeled pixel samples (hard labels) for training. It is a challenging task since the classification process is susceptible to over-fitting under training with limited samples. To relieve this problem, we propose a method based on dual hierarchical learning. First, we employ a connectionist hyperspectral convolution (HC) network to capture the representations of the pixels from different receptive fields. Specifically, an HC is designed to learn the correlation among adjacent pixels and is further extended to a connectionist hierarchical structure. These operations use the correlation to enhance one-pixel learning from multiple receptive fields. Second, we analyze the properties in the hyperspectral image and introduce a hierarchical pseudo label generation algorithm to enrich the supervision of the label information. Finally, we design a dual hierarchical learning strategy to help all HC layers learn from both the hard labels and the hierarchical pseudo labels. In other words, it addresses the HSI classification problem from different views. For inference, we employ two fusion strategies to find a better prediction. The experimental results on four popular HSI benchmarks, i.e. , Salinas-A, IndianPines, PaviaU, and PaviaC, demonstrate the effectiveness of the proposed method. Our code is publicly available on GitHub: https://github.com/ShuoWangCS/HSI-DHL.

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Cloud Implementation of Multinomial Logistic Regression for UAV Hyperspectral Images

Cited by 28 publications

References 70 publications

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Boosting Hyperspectral Image Classification with Dual Hierarchical Learning

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