This work is mainly related to hyperspectral image classification, with special emphasis on high-dimensional feature vectors. Various techniques and frameworks have been developed to tackle the HSI classification problem. Some of the recent HSI classification techniques can be found in [Yi Chen, Nasser M. Nasrabadi] [12]–[17]. In here, we just emphasize the most recent prominent technique in HSI. A. Dimensionality Reduction With regard to the issue that we are following, there are another popular examples based on dimensional reduction studies, Principal Component Analysis (PCA), Random Projection (RP) that can project the data matrix into another space which is lower dimensional rather than original space [18]. Structurally, in these …show more content…
However, since CNNs have been mostly considered on image and visual-related problems, there are a few prominent works for HSI classification based on deep learning. Chen et al. [36] used deep belief network (DBN) to extract spectral-spatial features for the HSI classification. Yuan et al. [37] applied the CNN model which proposed by Dong et al. [20] on the hyperspectral images. In their work, they didn’t consider preserving the spectral information, and they treat hyperspectral images as a RGB images. Hu et al. [30] presented a new CNN architecture that comprises of an input layer, a convolutional layer, a maxpooling layer, a fully-connected layer, and finally an output for hyperspectral image classification. They proposed CNN to directly classify hyperspectral data in the spectral domain. Makantasis et al. [31] presented a deep learning based classification method that hierarchically constructs high level features automatically. Wu et al. [22] developed a novel
imaging application. This is because the crucial data required for the classification phase are derived at this stage. Feature extraction is the process of estimating
Abstract— Dimensionality Reduction is a key issue in many scientific problems, in which data is originally given by high dimensional vectors, all of which lie however over a fewer dimensional manifold. Therefore, they can be represented by a reduced number of values that parameterize their position over the mentioned non-linear manifold. This dimensionality reduction is essential not only for representing and managing data, but also for its understanding at a high interpretation level, similar to the way it is performed by the mammal cortex. This paper presents an algorithm for representing the data that lie on a non-linear manifold .
Survey that the commence structure in (1) can be deciphered as section astute low-dimensional representation for each illustration; as needs be it can be speedily acclimated to fit our demand. Specifically, we settle (3) for the entire dataset, and utilize the start system W0 as the novel representation and support it into the data section module, with the subscript "0" demonstrating the entire dataset. It is critical that here we support using W0 over customary name change strategies, for instance, CPLST [32] for the going with reasons: 1) the proposed procedure does not rely on upon the certifiable stamp system Y as in the arrangement of CPLST, and 2) test relationship can be unequivocally introduced, which is proper for data distribute. Our approach makes no particular doubts on the choice of section counts, in this way unique procedures can be considered, including k-suggests gathering, area tricky hashing (LSH), and some flexible systems, for instance, Affinity Propagation batching or ISODATA , if satisfactory prior learning is available. In our execution, we use k-suggests gathering for its straightforwardness and
These 16 features included 12 features calculated based on the 6 multispectral bands, which is mean value and standard deviation of these bands. In addition, we chose intensity, texture-variance, texture-mean, and NDVI (Normalized Difference Vegetation Index) for classifications. Finally, training samples were selected for each classification category based on the previously segmented and merged objects
In the data set, there is lack of redundancy of data given the orthogonal components and it enhances the efficiency on the processes that are taking place in the smaller dimension.
The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,…,k} is its label .k is the number of categories. l is the number of labeled data in each category, and u is the number of unlabeled data. Our method aims to learn a high-level image representation S by exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into different classifiers to obtain final classification results. The procedure of semisupervised feature learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,…..,T}
We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.
The recent development ensures the popularity of CBIR, since it has been applied in many real world applications such as life sciences, environmental and health care, digital libraries and social media such as facebook, youtube, etc. CBIR understands and analyzes the visual content of the images [20]. It represents an image using the renowned visual information such as color, texture, shape, etc [11, 12]. These are often referred as basic features of the image, which undergoes lot of variations according to the need and specifications of the image [7-9]. Since the image acquisition varies with respect to illumination, angle of acquisition, depth, etc, it is a challenging task to define a best limited set of features to describe the entire image library.
However, all have their advantages and pitfalls. Spectral matching based methods provide high detection accuracy but not suitable for camouflage detection due to unavailability of spectral signatures of camouflaged targets. Spectral anomaly and ICA based methods have been reported mostly on synthetic and Visible-SWIR Hyperspectral data with varying performance accuracy depending upon the level of spectral variability13, 21. The real-world conditions always differ from synthetic data due to nonlinear atmospheric attenuation, background clutter and sensor noise. Moreover, there exists the poor spectral contrast between the camouflaged objects and natural background. As a result, target detection accuracy drops down and false alarm rate increases. Therefore, there is a need to develop a method for efficient and robust detection of camouflaged targets in MWIR spectral region, which is mostly used in reconnaissance and surveillance
Abstract— Nowadays, with the increase in the dreadful diseases, huge amount of database is produced in hospitals and are exponentially increasing day by day. Utilizing these medical images after efficient classification plays a major role in case based reasoning and supports in clinical decision making. Therefore, it is important to classify these images and access them accurately. A modality classifier helps to classify the medical images based on the modality. In our study, we analyzed spatial and spectral features of Magnetic Resonance (MR) images and Computer Tomography (CT) scan and also the fusion of these features is performed. It is found that these modalities have different characteristics which help in classification. Initially, these images are preprocessing using the Median filter and spatial and spectral features were extracted and feature fusion is performed. The
Using Singular Value Decomposition (SVD) a tensor \boldsymbol{D} can be decomposed into a diagonal matrix with three non-negative elements \lambda_{1}, \lambda_{2} and \lambda_{3}, known as the eigenvalues, and a matrix composed of three orthogonal eigenvectors \boldsymbol{e}_{1}, \boldsymbol{e}_{2}, and \boldsymbol{e}_{3}, known as the eigenvectors. Tensor \boldsymbol{D} is usually represented by an ellipsoid such that the lengths of the major axes of the ellipsoid are proportional to the square roots of the eigenvalues, and the directions of the three major axes of the ellipsoid correspond to the three eigenvectors.
Also, we evaluate the extent to which the samples and methods used are able to capture the random changes realized in the data obtained.
been applied was used by van Diest et al.[10]. The usage of Support Vector Ma-
Unlike traditional statistical methods of data analysis which are primarily concerned parameter estimation, topological data analysis regards the data as a sample from a manifold embedded in euclidean space and attempts to recover topological features such as connectedness or the number of holes. An advantage of considering topology is that it is stable under deformations, and can therefore be said to be insensitive to errors introduced in the sampling [].
In the proposed fusion framework, IHS transform is used to separate intensity component from MS images firstly. Then, SFIM and wavelet transform are applied to the intensity component of MS images and the Pan image to build the multi-scale representations which include the low- and high frequency sub-images in different scales, While SFIM can effectively preserve images’ spectral properties. Since the low- and high-frequency sub-images obtained from wavelet decomposition have different information of images, we process low- and high frequency sub-images with different strategies. At last, the inverse wavelet transform (IWT) and inverse IHS transform are applied to perform the fusion tasks. The visual and statistical analysis on the experimental results for Worldview-2 images shows the effectiveness of our proposed method.