We present a method for automatically registering images based on nonlinear compression. The method involves three steps: (i) analysis of the complexity of the images, (ii) high level compression for extracting control points in the images, (iii) registration of the images by matching control points. The first step analyzes the complexity of the given images. It numerically computes from any image a complexity index which determines the efficiency at which the image can be compressed. This index is used in the second step of the algorithm to select coefficients in the wavelet representation of the image to produce a highly compressed image. The wavelet coefficients of the highly compressed image are then transformed to pixel values. Only a few pixel values are nontrivial. The third stage of the algorithm uses a point alignment technique to identify matching control points and to erect the registering transformations. The algorithm is tested on two quite different scenes: a portrait, representing an uncomplicated scene, and a Landsat TM image of the Pacific Northwest. In both cases, images are tested which differ by a rotation and which differ by a rigid transformation. The algorithm allows a choice of different metrics in which to do the compression and selection of control points.
The need for fast, accurate, and reliable image registration techniques is increasing primarily due to the large amount of remote sensing data which will be generated by future Earth and space missions and the diversity of such data in temporal, spatial and spectral components. Registration of the remote sensing imagery is one of the most important steps in view of further processing and interpretation of such data since the information fusion from multiple sensors start with the registration of the data. Traditional approaches to image registration require substantial human involvement in the selection and matching of the ground control points in the reference and input data sets. Considering the dramatic increase that is predicted in the volume of remote sensing data that will be collected during future missions, it is imperative that fully automatic registration algorithms be utilized. We present a three-step approach to automatic registration of remote sensing imagery. The first step involves the wavelet decomposition of the reference and input images to be registered. In the second step, we extract domain independent features to be used as the control points from the low-low components of the wavelet decompositions of the reference and input images employing the Lerner algebraic edge detector (LAED) and the Sobel edge detector. Finally, we utilize the maxima of the low-low wavelet coefficients preprocessed by the edge detectors and an exclusive-or based similarity metric to compute the transformation function. We illustrate the effectiveness of the proposed registration method on a Landsat thematic mapper image of the Pacific Northwest, and show that the performance of the LAED is superior to that of the Sobel edge detector.
The motivation behind this research has been to identify, and where possible, minimize or eliminate potential problem areas facing NASA in its mission of gathering and analyzing remotely-sensed imagery in both Earth and space disciplines. Managing and extracting useful information from the massive image databases resulting from such missions is a challenging task for NASA. The key to real-time archival and retrieval of this massive image data lies in the notion of content based image data management. Two major steps are involved in this process. The first one is to automatically extract image content or meta-data from satellite imagery. The second one is to organize this database to permit users from numerous disciplines and communities to access data relevant to their needs. Accordingly, each data set is indexed in multiple ways, enabling users to retrieve images by specifying constraints over a combination of attributes. One such method provides users the ability to search the data holdings using a metric of similarity in content so that neighboring images in the database have a high probability of hit when queried for a specific type of meta- data content. An important area of research is therefore to compte and evaluate similarity measures for images. In this paper, we present a back propagation neural network based technique to classify a multispectral satellite image and extract a similarity measure using the meta-data classification content.
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