Deep Learning for Classification of Hyperspectral Data: A Comparative Review

Audebert, Nicolas; Saux, Bertrand Le; Lefèvre, Sébastien

doi:10.1109/mgrs.2019.2912563

Cited by 499 publications

(251 citation statements)

References 76 publications

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“…Machine learning (ML) techniques have been introduced for HSI data classification [ 10 ], which have been collected in an extensive list of detailed reviews, such as Li, et al [ 11 , 12 ]. The ML field has experienced a significant revolution thanks to the development of new deep learning (DL) models since the early 2000s [ 13 ], which is supported by advances in computer technology.…”

Section: Introductionmentioning

confidence: 99%

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products

Zhu

Gowen

Feng³

et al. 2020

Sensors

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Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

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Section: Introductionmentioning

confidence: 99%

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products

Zhu

Gowen

Feng³

et al. 2020

Sensors

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“…In the studies carried out, a limited number of data sets that are accessible to common use are used. The low number of samples in hyperspectral imaging poses a major challenge for implementing machine learning methods [27]. Deep networks usually need a great number of training samples to optimize the model parameters [29].…”

Section: Datasets Of Hyperspectral Imagesmentioning

confidence: 99%

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

Özdemir¹,

Polat²

2020

JIEC

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Since the acquisition of digital images, scientific studies on these images have been making significant progress. The sizes and quality of the images obtained have increased greatly from past to present. However, when the information contained in these images remains on the visible band (RGB band), the results that can be obtained are limited. For this reason, the need to acquire images with more broadband information has emerged. Hyperspectral Imaging (HSI) method has been developed to meet this need. A hyperspectral image consists of reflections in hundreds of different bands of the electromagnetic spectrum. Each object exhibits a unique reflection characteristic. Due to this characteristic, objects can be separated from each other using hyperspectral imaging. Hyperspectral cameras are used to obtain this image. The information it contains is much more than an RGB image, so deeper results can be achieved than the human eye can see. In this respect, it has great importance. Artificial intelligence technologies are used extensively in image processing as well as in many other fields. As a result, classification studies are carried out on hyperspectral images with machine learning methods. Machine learning methods can be considered as the most general terms of supervised machine learning, unsupervised machine learning, and reinforced machine learning. Supervised machine learning methods mainly: Support Vector Machines(SVM), k-Nearest Neighborhood(k-NN), Decision Trees, Random Forest, Linear Regression and Neural Networks(NN). Neural networks are also used as an unsupervised learning method. However, Deep Learning, a specialized method of artificial neural networks, is highly preferred due to its unique structure. Artificial intelligence methods have been widely used in recent years, especially for the classification of hyperspectral images containing complex information. Considering the studies, it is seen that especially deep learning is used intensively. At this point, studies have revealed different types of models. The number of models and their successes are increasing day by day.

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“…In addition, considering the shading component of hyperspectral data is uncorrelated with the material of the imaged object, an intrinsic image decomposition approach was proposed in [25]. Recently, deep learning (DL) has been introduced to spectral-spatial classification of HSI [26], [27] and gained great concern, as the DL based models can automatically learn hierarchical features from the raw data [28].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Extraction Based on Convolutional Sparse Decomposition for Hyperspectral Image Classification

Zhong

Zhang

2020

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

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Due to the different spatial properties presented by various ground objects in hyperspectral image (HSI), multiscale-based feature extraction approaches have been developed for HSI classification in recent years. However, the spatial features of different scales are usually acquired at the cost of obscuring the structural information of input image, which severely limits the effectiveness of multiscale strategy. In this paper, a convolutional sparse decomposition (CSD) model is introduced to characterize the significant spatial structures of hyperspectral data while removing the irrelevant noise and local textures at the specific scale. Based on the CSD model, a multiscale spectral-spatial feature extraction framework is generated, which consists of the following steps. First, spectral dimensionality of the original HSI is reduced through a segmented averaging approach. Second, spatial features at different scales are separated from the dimension-reduced data by solving the CSD model with different regularization parameters. Finally, principal component analysis (PCA) is performed and the obtained multiscale spectral-spatial features are stacked together for classification. Experiments conducted on three widely used hyperspectral datasets demonstrate that the proposed method is robust in capturing effective features of ground objects at different scales, and leads to better classification results than several state-of-the-art methods.

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Deep Learning for Classification of Hyperspectral Data: A Comparative Review

Cited by 499 publications

References 76 publications

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

Multiscale Feature Extraction Based on Convolutional Sparse Decomposition for Hyperspectral Image Classification

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