An interactive tool for semi-automatic feature extraction of hyperspectral data

Abstract

Abstract:The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in -Hyperspectral Data Analyst (HypDA) -for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

“…FE techniques for HSI analysis can be either supervised, such as embedded feature selection with support vector machines (EFS-SVM) [9], linear discriminant analysis (LDA) [10], or unsupervised, including principal component analysis (PCA) [11], mainfold learning [12], and maximum noise factoring (MNF) [13]. There are a number of recent research studies that address the effect of different feature extraction techniques on HSI analysis [14][15][16]. Similar to unsupervised classification, unsupervised feature extraction methods are used in the absence of labeled training data.…”

Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories

“…Another way of using the images is the application of spectral features: as all surface objects have a given and specific spectral profile, we can use it to determine measurable quantities (Kovács -Szabó 2016). A spectral band alone rarely corresponds with a measurable quantity, in conclusion band ratios are often used in remote sensing studies.…”