Hyperspectral Data Analysis in R: The hsdar Package

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data

Urban morphology characterization from earth observation and land use classification for applications in urban climate models

We demonstrate (1) the use of earth observation in providing urban morphological and vegetation parameters needed by urban climate models, (2) an objective approach to create two different land use classifications based on the morphological parameters, and (3) the comparability of the new approach with in-situ derived land use classes and physical parameters (reference run). The new approach is developed and tested for Munich, Germany by integrating the obtained land use classifications and physical parameters into a 3-dimensional urban climate model (MUKLIMO_3). The model results of a case study are compared to the current approach (reference run) and evaluated against continuous and temporal meteorological measurements. Eight morphological parameters (building surface fraction, wall-area index, building height, impervious surface fraction, leaf area index and vegetation cover of low vegetation, tree cover, as well as tree height) were derived using 3 different earth observation data sets. An IKONOS scene of 17th September 2003, a Rapid Eye scene of 27th July 2009 and stereo images from the High Resolution Stereo Camera of 15th September 2004 were used to derive the urban morphological parameters. The accuracy and spatial resolution of the input data sets is sufficient for use in MUKLIMO_3. The two different land use classifications were prepared through the use of GAP statistic and cluster analysis based on (i) urban structure types of Munich and (ii) MUKLIMO_3 model grid. A comparison of the three different land use classifications with aerial photographs of the measurement stations shows that the classifications based on earth observation describe the land cover of the stations surrounding more realistically then the in-situ classification

Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details