Abstract
Abstract. Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.
Highlights
Remote sensing is the science of acquiring reflected energy from an object by sensors, without the sensor being in contact with it
This study deals with end member in the Sunderbans Delta of West Bengal, India by spectrally unmixing the mixed pixels by applying N-FINDR and Linear Spectral Unmixing algorithms to the hyperspectral image and injecting only the spectral sensitive hyperspectral bands into the multispectral band for fusion for accurate retrieval of spectral signatures
An EO-1 Hyperion image of the study area has been procured from the USGS Earth Resources Observation and Science (EROS) Center through Data Acquisition Request (DAR) on the 27th of May, 2011
Summary
Remote sensing is the science of acquiring reflected energy from an object by sensors, without the sensor being in contact with it It provides us with data covering various spectral and spatial resolutions. We fuse the multispectral and hyperspectral image to obtain an image of higher spatial and spectral resolution, which would result in more accurate end member detection, which is our motivation for this study. Most of the initial research was based on Pan sharpening like PCA based techniques (Licciardi et al 2012), CN sharpening, Gram-Schmidth Sharpening (Maurer, 2013 ) These methods basically improve the photo interpretation of the image but they are not efficient for image analysis and spatial study. This study tries to overcome these shortcomings and obtains an image which has spectral signature of the original hyperspectral image and spatial resolution of the multispectral image
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