Hyperspectral Data Analysis for Arid Vegetation Species : Smart &amp; Sustainable Growth

Borana, S. L.; Yadav, Shailesh Kumar; Parihar, Sangeeta

doi:10.1109/icccis48478.2019.8974502

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…CD is the process used to identify and analyze differences between images of the same area taken at different times. This can be used for agricultural monitoring [1], resource exploration, land-change monitoring, potential anomaly identification [2][3][4], and various other applications. With the help of rich spectral information, HSI CD has the potential to identify finer changes.…”

Section: Introductionmentioning

confidence: 99%

Spectral–Temporal Transformer for Hyperspectral Image Change Detection

Ding

2023

Remote Sensing

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Deep-Learning-based (DL-based) approaches have achieved remarkable performance in hyperspectral image (HSI) change detection (CD). Convolutional Neural Networks (CNNs) are often employed to capture fine spatial features, but they do not effectively exploit the spectral sequence information. Furthermore, existing Siamese-based networks ignore the interaction of change information during feature extraction. To address this issue, we propose a novel architecture, the Spectral–Temporal Transformer (STT), which processes the HSI CD task from a completely sequential perspective. The STT concatenates feature embeddings in spectral order, establishing a global spectrum–time-receptive field that can learn different representative features between two bands regardless of spectral or temporal distance, thereby strengthening the learning of temporal change information. Via the multi-head self-attention mechanism, the STT is capable of capturing spectral–temporal features that are weighted and enriched with discriminative sequence information, such as inter-spectral correlations, variations, and time dependency. We conducted experiments on three HSI datasets, demonstrating the competitive performance of our proposed method. Specifically, the overall accuracy of the STT outperforms the second-best method by 0.08%, 0.68%, and 0.99% on the Farmland, Hermiston, and River datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

Spectral–Temporal Transformer for Hyperspectral Image Change Detection

Ding

2023

Remote Sensing

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“…de/en/kat/main-research/datacenter/bearing-datacenter/data-sets-and-download/, accessed on 21 October 2023) [35]. The two datasets are conducted by some fault diagnosis methods but it is difficult to achieve high diagnostic accuracy [14,25,33,[36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

Zheng,

Chen,

et al. 2023

Sensors

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The application of edge computing combined with the Internet of Things (edge-IoT) has been rapidly developed. It is of great significance to develop a lightweight network for gearbox compound fault diagnosis in the edge-IoT context. The goal of this paper is to devise a novel and high-accuracy lightweight neural network based on Legendre multiwavelet transform and multi-channel convolutional neural network (LMWT-MCNN) to fast recognize various compound fault categories of gearbox. The contributions of this paper mainly lie in three aspects: The feature images are designed based on the LMWT frequency domain and they are easily implemented in the MCNN model to effectively avoid noise interference. The proposed lightweight model only consists of three convolutional layers and three pooling layers to further extract the most valuable fault features without any artificial feature extraction. In a fully connected layer, the specific fault type of rotating machinery is identified by the multi-label method. This paper provides a promising technique for rotating machinery fault diagnosis in real applications based on edge-IoT, which can largely reduce labor costs. Finally, the PHM 2009 gearbox and Paderborn University bearing compound fault datasets are used to verify the effectiveness and robustness of the proposed method. The experimental results demonstrate that the proposed lightweight network is able to reliably identify the compound fault categories with the highest accuracy under the strong noise environment compared with the existing methods.

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“…Hyperspectral remote sensing image is characterized by high dimension, high resolution, and rich spectral and spatial information [1], which have been diffusely used in numerous real-world tasks, such as sea ice detection [2], ecosystem monitoring [3,4], vegetation species analysis [5] and classification tasks [6,7]. With the speedy progress of remote sensing technology and artificial intelligence (AI), a great proportion of new theories and methods in deep learning have been proposed to handle the challenges and problems faced by the field of hyperspectral image [8].…”

Section: Introductionmentioning

confidence: 99%

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

et al. 2021

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.

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Hyperspectral Data Analysis for Arid Vegetation Species : Smart & Sustainable Growth

Cited by 13 publications

References 38 publications

Spectral–Temporal Transformer for Hyperspectral Image Change Detection

Spectral–Temporal Transformer for Hyperspectral Image Change Detection

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

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