Abstract
Abstract. Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images.
Highlights
Remote sensing has many applications like crop classification, pollution control, resource management, etc
Linea mixing model (LMM) is widely used for spectral unmixing of hyperspectral images since it is simpler and in most cases there is no interaction between materials in the scene (Keshava and Mustard, 2002)
There are some works in the literature to enhance spatial resolution of hyperspectral images using spatial information available in PAN image
Summary
Remote sensing has many applications like crop classification, pollution control, resource management, etc For these purposes, hyperspectral sensors are strong tool as they can determine chemical and physical composition of objects. Against high spectral resolution of hyperspectral images, these images have low spectral resolution Linea mixing model (LMM) is widely used for spectral unmixing of hyperspectral images since it is simpler and in most cases there is no interaction between materials in the scene (Keshava and Mustard, 2002). Gross et al (1998) (Gross and Schott, 1998) proposed a fusion method for image sharpening using spectral mixture analysis. In this paper we have used spectral unmixing results and Panchromatic Image
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