PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

Uddin, Md. Palash; Mamun, Md. Al; Hossain, Md. Ali

doi:10.1080/02564602.2020.1740615

Cited by 185 publications

(75 citation statements)

References 65 publications

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“…To select a learning framework to evaluate the recognition performance of the proposed method, comparative experiments were performed on five different learning models including PCA based SVM, because PCA based SVM method is widely used in the motion classification. In this paper, the learning frameworks considered are GoogleNet, ResNet, VGG, AlexNet, and PCA based SVM [ 34 , 39 , 40 , 51 , 52 , 53 ]. Comparative recognition performance is summarized in Table 2 .…”

Section: Recognition Resultsmentioning

confidence: 99%

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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Section: Recognition Resultsmentioning

confidence: 99%

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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“…[ 7 , 8 ], which reduce the dimensions of the extracted features. Commonly used methods include principle component analysis (PCA) and independent component analysis (ICA), manifold learning [ 9 , 10 , 11 ], and other algorithms. At the end of the process, the dimensionality-reduced features are sent to the classifier to classify the fault.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Wang

Chen

Liu

et al. 2021

Sensors

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Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

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“…From the geological point of view, it is necessary to reduce the unnecessary or redundant bands to enhance those bands having diagnostic absorption features of some specific minerals (Gupta and Venkatesan, 2020;Samani et al, 2020;Shirmard et al, 2020). Previously, feature extraction or data dimensionality reduction methods were extensively used for mineral mapping using spaceborne and airborne hyperspectral datasets like Hyperion and AVIRIS (Boardman and Kruse, 1994;Uddin et al, 2020). Recently launched PRISMA and DESIS datasets with good spectral quality in bandwidth and SNR can prove to be a boon for geoscientists in mapping the remote and geologically rich areas on Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Desis and Prisma Hyperspectral Remote Sensing Datasets for Geological Applications

Tripathi

Garg

2021

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

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Abstract. With the recent launch of advanced hyperspectral satellites with global coverage, including DESIS and PRISMA, a massive volume of high spectral resolution data is available. This work is focused on the spectral analysis and implementation of feature extraction or data dimensionality reduction techniques on both newly available datasets for geological interpretation. Three of the best feature extraction algorithms, Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were tested for lithological mapping for the Rajasthan state of India. The present work demonstrates the advantage of the feature extraction algorithm in geological mapping and interpretability as it shows the excellent performance for these datasets. The narrowband ratios for the clay minerals, dolomite, kaolinite, amphiboles, and Al-OH are generated using the PCA and MNF components. All of these band ratios were compared with the Lithological Map available. It is concluded that PCA is the first choice for feature-based lithological classification of hyperspectral remote sensing data. ICA is giving some impressive results which can be explored further. DESIS and PRISMA have a 30 km swath, finer spectral resolution, and high signal-to-noise ratio, which shows much potential in lithological mapping over the parts of northern India. It is suggested to use advanced feature extraction algorithms with recently launched hyperspectral data for accurate and updated mineral mapping over a sizeable geological importance area.

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PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

Cited by 185 publications

References 65 publications

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Feature Extraction of Desis and Prisma Hyperspectral Remote Sensing Datasets for Geological Applications

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