Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense...

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insec...

Hyperspectral imaging has emerged as a promising tool in the identification of objects and the state of objects, by their chemical and material composition. Hyperspectral imaging acquires spectral information at each pixel location across a wide range of the light spectrum. This enhanced spectrum information also comes with additional noise including spectral mixing, blurring and acquisition di...

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. We introduced and tested a multiview hyperspectral imaging technique for noninvasive topographic im...

Pigments absorb light, transform it into energy, and provide reaction sites for photosynthesis; thus, the quantification of pigment distribution is vital to plant research. Traditional methods for the quantification of pigments are time-consuming and not suitable for the high-throughput digitization of rice pigment distribution. In this study, using a hyperspectral imaging system, we developed ...

In this paper, we will investigate techniques for the combination of image fusion and segment based image analysis. We describe a color preserving iconic fusion with subsequent segmentation and classification, a ‘cookie cutter’ approach for the integration of high resolution RGB and low resolution hyperspectral image data and a decision based fusion technique, that combine high resolution panch...

Introduction. More and more image acquisition systems record high-dimensional vectors for each pixel, as it is the case for hyperspectral imaging or dynamic PET imaging, to cite only them. In hyperspectral imaging, each pixel is associated with a light-spectrum containing up to several hundreds of radiance values, each corresponding to narrow spectral bands. In dynamic PET images, each pixel is...

There are a number of virtual dimension typical targets in hyperspectral image. Determining the virtual dimension is the first step in many applications of hyperspectral image. In view of the virtual dimension calculation method of having high time complexity problem, according to the calculation of highly parallel features, in this paper graphics processing unit (GPU) using the Compute Unified...

hyperspectral images. Proceedings of IEEE International, Geoscience and Remote Sensing Symposium. IGARSS 04, 5:3257 – 3260, Sept. 2004. [2] M. Estlick M. Leeser, J. Theiler and J. J. Szymanski. Design tradeoffs in a hardware implementation of the K-Means clustering algorithm. Proceedings of the IEEE. Sensor Array and Multichannel Signal Processing Workshop., pages 520 – 524, March 2000. [3] Sam...

This paper briefly describes the methods available for collection, atmospheric and geometric correction, and processing of hyperspectral imagery. Discussion of data capture concentrates on logistics of integrating image acquisition with field data collection. Atmospheric correction is required to use the imagery with reference spectra from field and laboratory sensors; a variety of methods for ...