A spectral-spatial kernel-based method for hyperspectral imagery classification

Li, Li; Ge, Hongwei; Gao, Jianqiang

doi:10.1016/j.asr.2016.11.006

Cited by 31 publications

(9 citation statements)

References 35 publications

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“…In this study, two widely used hyperspectral datasets including the Salinas [ 59 , 60 , 61 ] and Indian Pines [ 59 , 62 , 63 ] image datasets were used ( Table 3 ) and divided into validation, train and test samples ( Figure 3 ). Both datasets contain noisy bands due to dense water vapour, atmospheric effects, and sensor noise.…”

Section: Methods and Datasetmentioning

confidence: 99%

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

Roodposhti

Aryal

Lucieer

et al. 2019

Entropy

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Uncertainty assessment techniques have been extensively applied as an estimate of accuracy to compensate for weaknesses with traditional approaches. Traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. Here, we apply and compare two uncertainty assessment techniques that do not rely on test data availability and enable the spatial characterisation of classification accuracy before the validation phase, promoting the assessment of error propagation within the classified imagery products. We compared the performance of emerging deep neural network (DNN) with the popular random forest (RF) technique. Uncertainty assessment was implemented by calculating the Shannon entropy of class probabilities predicted by DNN and RF for every pixel. The classification uncertainties of DNN and RF were quantified for two different hyperspectral image datasets—Salinas and Indian Pines. We then compared the uncertainty against the classification accuracy of the techniques represented by a modified root mean square error (RMSE). The results indicate that considering modified RMSE values for various sample sizes of both datasets, the derived entropy based on the DNN algorithm is a better estimate of classification accuracy and hence provides a superior uncertainty estimate at the pixel level.

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Section: Methods and Datasetmentioning

confidence: 99%

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

Roodposhti

Aryal

Lucieer

et al. 2019

Entropy

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“…Although there are many approaches for intrusion detection in network traffic, such as clustering-based techniques or support vector machines (SVM), they mostly have the disadvantage of long training times. Moreover, they normally need parameter tuning and [15][16][17] and [18], and do not have a satisfactory performance in multiclass classification. Hence, in this section, we focus on machine learning based intrusion detection techniques.…”

Section: Literature Surveymentioning

confidence: 99%

“…This measure is the harmonic mean of the precision and recall [27]. It is calculated based on the equation; the formula is calculated (18).…”

Section: ) Accuracymentioning

confidence: 99%

Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine

2021

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“…Let X be the input image, and I obtained above be the guidance image. Build a local linear model between the output of X and I according to (1), get the values of a k and b k from (4), calculate the value of each pixel by (6), and finally get the output.…”

Section: Spatial-spectral Feature Extractionmentioning

confidence: 99%

“…To solve these problems, on the one hand, many researchers engaged in HSI classification use methods of machine learning for image classification, including support vector machine (SVM) [6], Gaussian mixture model [7], random forest (RF) [8], sparse expression [9], active learning [10], etc. On the other hand, deep learning has been successfully applied in computer vision and other fields.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Joint Classification of Hyperspectral Image With Locality and Edge Preserving

Zhang

Liu

Huanhuan

2020

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

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Hyperspectral image (HSI) classification is an important part of its processing and application. Aiming at the problems of high data dimensionality and high spatial neighborhood correlation in HSI classification, we propose a spatial-spectral joint classification method of HSI with locality and edge preserving in this article. First, the input HSI is normalized, and the feature is extracted by principal component analysis. The first principal component image is taken as the guidance image. Second, guided filtering is used to extract the spatial features of each band separately. Then, the extracted spatial features are superimposed, and low-dimensional embedding is completed through local Fisher discriminant analysis. Finally, the obtained low-dimensional embedded features are input into a random forest classifier to get classification results. The experimental results of two HSI show that the proposed method achieves higher classification accuracy than other related methods. In the case of randomly selecting 10% and 1% samples from each class of ground object as training samples, the overall classification accuracy is improved to 99.57% and 97.79%, respectively. This method effectively uses the spatial and local information of the image in low dimensional embedding, and preserves the boundaries of the ground objects, thus improving the classification effect.

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A spectral-spatial kernel-based method for hyperspectral imagery classification

Cited by 31 publications

References 35 publications

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine

Spatial-Spectral Joint Classification of Hyperspectral Image With Locality and Edge Preserving

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