Hyper-Spectral Content Aware Resizing Jesse Scott 1 , Richard Tutwiler 2 , Michael Pusateri 1 1 Electronic and Computer Services Pennsylvania State University 149 Hammond Building University Park, PA 16802 2 Applied Research Laboratory Pennsylvania State University 450 Science Park Road University Park, PA 16802 Abstract- Image resizing is performed for many reasons in image processing. Often, it is done to reduce or enlarge an image for display. It is also done to reduce the bandwidth needed to transmit an image. Most image resizing algorithms work based on principles of spatial or spatial frequency interpolation. One drawback to these algorithms is that they are not image content aware and can fail to preserve relevant features in an image, especially during size reduction. Recently, a content aware image resizing algorithm, called seam carving, was developed. In this paper we discuss an extension of the seam carving algorithm to hyper-spectral imagery. For a hyper-spectral image with an MxN field of view and with P spectral layers, our algorithm identifies a one pixel wide path through the image field of view containing a minimum of information and then removes it. This process is repeated until the image size is reduced to the desired dimension. Information content is assessed using normalized spatial power metrics. Several such metrics have been tested with varying results. The resulting carved hyper-spectral image has the minimum reduction in information for the resizing based upon energy metrics used to quantify information. We will present the results of seam carving applied to imagery sets of: three spectra RGB imagery from a standard still camera, two spectra imagery generated synthetically, and three spectra imagery captured with VNIR, SWIR, and LWIR cameras. I. INTRODUCTION Spatial image resizing is a fundamental problem with many viable solutions. The goal of resizing is to change the dimension of an image to fit a specific resolution or display while retaining as much information in the image as possible. Single image resizing has been the focus of scholarly works, such as [1][2][3][4][5][6], attempting to adjust an image for different sizes. A variety of approaches are attempted to improve the performance of image resizing. The most simplistic and ubiquitous version of spatial resizing is the scaling method, but it has the drawback of detail loss during spatial interpolation [9]. Cropping is used often for reduction of images for small displays like those on many mobile devices, but the main issue arising with the cropping is the inability to retain all pertinent information if the crop size is not big enough [9]. Other resizing methods are more promising, but also have some limitations. The saliency based methods [7][8] attempt to extract the important information from the scene, but the failure is that saliency does not always indicate the most important parts of the image. A promising resizing method is a content aware image resizing algorithm called seam carving [1]. Seam carving has been implemented for visible band grayscale images and has also been extended to color imagery. Both processes use a grayscale version of the image to generate the seams for carving and then carve the seams from all layers uniformly. We propose an extension to multi and hyper- spectral imagery that generates the algorithm’s spatial power metric based upon all the spectral layers of the image; our objective is to extend the content awareness of the algorithm to encompass the various modalities available within a multi-spectral image. II. BACKGROUND Many methods have been presented in the literature for single image resizing including cropping, scaling, and various resizing techniques [3][4][5][6][7][8]. When selecting a method, a core requirement is the pixel correlation between layers. If a method reduces the size of each layer of the image differently, the spatial correlation between layers is lost. The subsections A through C and figure 1 present two fundamental methods along with the seam carving approach. A. Cropping Cropping is a simple and common method for resizing images. Cropping selects a window of the image for preservation and removes any pixels outside the window. However, the cropping method often sacrifices some important regions in order to retain other important portions of the image as illustrated in figure 1. There are automated cropping techniques [7][8][10] that retain specified regions, such as faces, but require significant preprocessing to select the regions of interest (ROI).