Dear Editor, This letter presents an open-set classification method of remote sensing images (RSIs) based on geometric-spectral reconstruction learning. More specifically, in order to improve the ability RSI model adapt environment, geometric and spectral feature fusion is proposed. proposes realize features with hyper-spectral light detection ranging (LiDAR) data for first time. In a variety s...

introduction in remote sensing studies, especially those in which multi-spectral image data are used, (i.e., landsat-7 enhanced thematic mapper), various statistical methods are often applied for image enhancement and feature extraction (reddy, 2008). principal component analysis is a multivariate statistical technique which is frequently used in multidimensional data analysis. this method atte...

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformation of the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) can be used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signal sources composing a given hyperspectral scene is generally much less than the number...

Cluster ensemble has been shown to be an effective thought of improving the accuracy and stability of single clustering algorithms. It consists of generating a set of partition results from a same data set and combining them into a final one. In this paper, we develop a novel cluster ensemble method named Cluster Ensemble algorithm using the Binary k-means and Spectral Clustering (CEBKSC). By u...

We show that the joint spectral radius of a set of matrices is strictly increasing as a function of the data in the sense that if a set of matrices is contained in the relative interior of the convex hull of an irreducible set of matrices, then the joint spectral radius of the smaller set is strictly smaller than that of the larger set. This observation has some consequences in the theory of ti...

The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named "automatic pre-processing method for Raman imaging data set (APRI)", which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm a...