Improved Feature Extraction, Feature Selection, and Identification Techniques That Create a Fast Unsupervised Hyperspectral Target Detection Algorithm

This research extends the emerging field of hyperspectral image (HSI) target detectors that assume a global linear mixture model (LMM) of HSI and employ independent component analysis (ICA) to unmix HSI images. Via new techniques to fully automate feature extraction, feature selection, and target pixel identification, an autonomous global anomaly detector, AutoGAD, has been developed for potential employment in an operational environment for real-time processing of HSI targets. For dimensionality reduction (initial feature extraction prior to ICA), a geometric solution that effectively approximates the number of distinct spectral signals is presented. The solution is based on the theory of the shape of the eigenvalue curve of the covariance matrix of spectral data containing noise. For feature selection, previously a subjective definition called significant kurtosis change was used to denote the separation between targets classes and non-target classes. This research presents two new measures, potential target signal to noise ratio (PT SNR) and max pixel score which computed for each of the ICA features to create a new two dimensional feature space where the overlap between target and non-target classes is reduced compared to the one dimensional kurtosis value feature space. Finally, after target feature selection, adaptive noise filtering, but with an iterative approach, is applied to the signals. The effect is a reduction in the power of the noise while preserving the power of the target signal prior to target identification to reduce false positive detections. A zero-detection histogram method is applied to the smoothed signals to identify target locations to the user. MATLAB code for the AutoGAD algorithm is provided

Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment

International audienc

Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition

Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition (SLRMD) has demonstrated to be an effective tool in HSI denoising. However, the matrix-based SLRMD technique cannot fully take the advantage of spatial and spectral information in a 3-D HSI data. In this paper, a novel group sparse and low-rank tensor decomposition (GSLRTD) method is proposed to remove different kinds of noise in HSI, while still well preserving spectral and spatial characteristics. Since a clean 3-D HSI data can be regarded as a 3-D tensor, the proposed GSLRTD method formulates a HSI recovery problem into a sparse and low-rank tensor decomposition framework. Specifically, the HSI is first divided into a set of overlapping 3-D tensor cubes, which are then clustered into groups by K-means algorithm. Then, each group contains similar tensor cubes, which can be constructed as a new tensor by unfolding these similar tensors into a set of matrices and stacking them. Finally, the SLRTD model is introduced to generate noisefree estimation for each group tensor. By aggregating all reconstructed group tensors, we can reconstruct a denoised HSI. Experiments on both simulated and real HSI data sets demonstrate the effectiveness of the proposed method.This paper was supported in part by the National Natural Science Foundation of China under Grant 61301255, Grant 61771192, and Grant 61471167, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the Science and Technology Plan Project Fund of Hunan Province under Grant 2015WK3001 and Grant 2017RS3024.Peer Reviewe

Summaries of the 4th Annual JPL Airborne Geoscience Workshop. Volume 2: TIMS Workshop

This is volume 2 of a three volume set of publications that contain the summaries for the Fourth Annual JPL Airborne Geoscience Workshop, held in Washington, D.C. on October 25-29, 1993. The main workshop is divided into three smaller workshops as follows: The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on October 25-26. The summaries for this workshop appear in Volume 1. The Thermal Infrared Multispectral Scanner (TIMS) workshop, on October 27. The summaries for this workshop appear in Volume 2. The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on October 28-29. The summaries for this workshop appear in Volume 3

Research on Multimodal Fusion Recognition Method of Upper Limb Motion Patterns

In order to solve the problems of single movement pattern recognition information and low recognition accuracy of multijoint upper limb exoskeleton rehabilitation training, a multimodal information fusion method with human surface electromyography (sEMG) and electrocardiogram (ECG) was proposed, and an Inception-Sim model for upper limb motion pattern recognition was designed. Integrating the advantages of multimodal information, inspired by the convolutional neural network processing image classification problem, the original signal was converted into a Gramian angular summation/difference fields-histogram of oriented gradient (GASF/GADF-HOG) image based on the principle of Grameen angle superposition/difference field, and the directional gradient histogram feature of the GASF/GADF image was extracted. The Inception-Sim model was constructed based on the Inception V3 model, and the human motion pattern recognition was completed on the basis of the transfer learning network. VGG16, ResNet-50, and other backbone networks were selected as comparison models. The recognition accuracy of each motion pattern for all participants reaches up to 90%, which is better than that of the control model. The average iteration speed of the proposed Inception-Sim model improved by about 21% compared to the control model. The experimental results show that the proposed multimodal information fusion recognition method can improve the accuracy and iteration speed of the upper limb motion recognition mode and then improve the effect of upper limb rehabilitation training