Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Roy, Swalpa Kumar; Mondal, Ranjan; Paoletti, Mercedes E.; Haut, Juan M.

doi:10.1109/jstars.2021.3088228

Cited by 57 publications

(38 citation statements)

References 36 publications

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“…The edge detection method mainly attempts to reduce the image data with the intention of data minimization. The pixel boundary can distinguish two boundaries in the image [13], [14]. In other words, edge detection reduces the data size and uses only the necessary data among all the data there is.…”

Section: Methodsmentioning

confidence: 99%

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

et al. 2022

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Section: Methodsmentioning

confidence: 99%

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

et al. 2022

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“…Many works have combined CNN with other machine learning techniques for HSI classification, such as transfer learning [39], ensemble learning [40], and few shot learning [41]. In addition, to fully extract the spatial features of HSI, morphological profiles were conducted on principal components and then followed by CNN to finish HSI classification task [42,43]. Very recently, Transformer has been investigated for HSI classification with CNN to extract spectral-spatial features [44].…”

Section: Related Work 21 Dcnn-based Hsi Classificationmentioning

confidence: 99%

Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

Huang

Chen

2021

Remote Sensing

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In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

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“…FC-3D-CNN achieves better results in a computationally efficient manner than SSRN due to the reduced spectral information used in the experimental process. Recently, Roy et al introduced trainable kernel for dilation and erosion operation to extract more meaningful morphological features from HSI and classify them in various land used and land covers [37].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Khan

Mazzara

Distefano

et al. 2022

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

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The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixel-wise classification challenging for conventional methods. To deal with nonlinearity issues in Hyperspectral Image Classification (HSIC), Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting Attention-fused Hybrid Network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using highlevel features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models.

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Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Cited by 57 publications

References 36 publications

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

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