scholarly journals Spatial Mutual Information Based Hyperspectral Band Selection for Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Anthony Amankwah
Keyword(s):  
Mutual Information ◽  
Supervised Classification ◽  
Band Selection ◽  
Support Vector ◽  
Spatial Dependency ◽  
Information Redundancy ◽  
Spectral Bands ◽  
Validation Criteria ◽  
Selection For

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results.

scholarly journals IMPROVING CLASSIFICATION OF MULTISPECTRAL IMAGES BASED ON SELECTED RELATIONS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 377-381
Author(s):  
L. Cohen ◽  
O. Almog ◽  
M. Shoshany
Keyword(s):  
Land Cover ◽  
Spectral Band ◽  
Research Area ◽  
Support Vector ◽  
Land Cover Types ◽  
Spectral Bands ◽  
Definition Of ◽  
Spectral Representations ◽  
The City

Abstract. A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.

scholarly journals Kernel Entropy Component Analysis-Based Robust Hyperspectral Image Supervised Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2823 ◽  
Author(s):  
Bing Tu ◽  
Chengle Zhou ◽  
Jin Peng ◽  
Wei He ◽  
Xianfeng Ou ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Supervised Classification ◽  
Component Analysis ◽  
Support Vector ◽  
Kernel Matrix ◽  
Training Set ◽  
Training Samples ◽  
Noisy Labels ◽  
Entropy Component

Recently, the “noisy label" problem has become a hot topic in supervised classification of hyperspectral images (HSI). Nonetheless, how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing. This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI, which consists of the following steps. First, the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability. Then, the eigenvectors and eigenvalues of the kernel matrix can be obtained by employing symmetric matrix decomposition. Next, the entropy corresponding to each training sample in each class is calculated based on entropy component analysis using the eigenvalues arranged in descending order and the corresponding eigenvectors. Finally, the sigmoid function is applied to the entropy of each sample to obtain the probability distribution. Meanwhile, a decision probability threshold is introduced into the above probability distribution to cleanse the noisy labels of training samples with mislabeled samples for each class. The effectiveness of the proposed method is evaluated by support vector machines on several real hyperspectral data sets. The experimental results show that the proposed KECA method is more efficient than other noisy label detection methods in terms of improving performance of the supervised classification of HSI.

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