Hyperspectral imagery classification with deep metric learning

Cao, Xin; Ge, Yubin; Li, Renjie; Zhao, Jing; Jiao, Licheng

doi:10.1016/j.neucom.2019.05.019

Cited by 36 publications

(18 citation statements)

References 27 publications

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“…Their experimental results on datasets revealed that their approach outperformed other traditional and deep learning methods, and also had the advantages of extracting homogeneous discriminative feature representations. The authors in [66] proposed a deep metric learning (DML) neural network for the classification of hyperspectral images. Their work aimed to decrease the distances between same classes and increase the distances between different classes by multilayers nonlinear projection.…”

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…For example, in [ 41 ], the authors presented a hybrid classification method which combines deep learning with SVM. Another example is the method based on deep metric learning presented in [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks

Książek

Romaszewski

Głomb

et al. 2020

Sensors

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In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area—blood stain classification—is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98–100% for the easier image set, and 74–94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57–71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem.

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“…And later, they further proposed a duplex metric learning [39] with two progressive metric learning stages for feature learning and image classification, respectively. In [40], Cao, et al proposed a DML-based hyperspectral image classification method. In this work, Mahalanobis distance was applied to compute the distance between the two samples on feature domain, and the distances results were used as the regularization term of the loss function to promote the discrimination of the features.…”

Section: Related Work Of Metric Learningmentioning

confidence: 99%

Improved Metric Learning With the CNN for Very-High-Resolution Remote Sensing Image Classification

Cheng

Shen

et al. 2021

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

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The number of labeled samples has a great impact on the classification results of very high-resolution (VHR) remote sensing image. However, the acquisition of available labeled samples is difficult and time-consuming. Faced with the limited labeled samples on high-resolution remote sensing image, semisupervised method becomes an effective way. In semi-supervised learning, accurate similarity prediction between unlabeled and labeled samples is very important. However, reliable similarity prediction between high-dimensional features is difficult. For more reliable similarity prediction for high-dimensional feature, a novel semi-supervised classification framework via improved metric learning (IML) with convolutional neural network (CNN) is proposed. In the proposed method, a novel trainable metric learning network is designed to accurately evaluate the similarity between high-dimension features. The vector distance parameter solving problem is transformed into a neural network design problem, which can automatically calculate parameters by BP algorithm. Finally, the pixel constraint mechanism is introduced to select the unlabeled samples. Experimental results conducted on three VHR remote sensing images, including Aerial, Xi'an, and Pavia University, and the results present that the proposed method performs better than the compared state-of-the-art methods.

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Hyperspectral imagery classification with deep metric learning

Cited by 36 publications

References 27 publications

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks

Improved Metric Learning With the CNN for Very-High-Resolution Remote Sensing Image Classification

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