Abstract
Abstract. Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm−1 to 1280 cm−1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm−1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.
Highlights
Sensed data acquired using hyperspectral sensors contains hundreds of spectral bands acquired over contiguous wavelength range
The Airborne thermal hyperspectral image usually is a challenge for conventional classification approaches
The component selection of minimum noise fraction (MNF) rotation transformation is analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the stack of LWIR and colour digital photograph
Summary
Sensed data acquired using hyperspectral sensors contains hundreds of spectral bands acquired over contiguous wavelength range Thermal hyperspectral technique opens up new possibilities in remote sensing. These imagers acquire data in long wave infrared region (8–12 μm). The usefulness of airborne LWIR hyperspectral data lies in the fact that the imagers can be flown in day as well as at night Mapping in this region has many advantages over visible region like detection of buried landmines and camouflage detection etc. In hyperspectral images there is a lot of redundancy in the data and storage problem as well This redundancy occurs due to high correlation between the bands. It becomes an important task to overcome this phenomenon To mitigate this problem we can reduce the data dimensions using data dimensionality reduction techniques like PCA, MNF, and ICA etc. The “curse of dimensionality” is dealt with data dimensionality reduction technique called minimum noise fraction MNF
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