J. Le Moigne

Automatic reduction of hyperspectral imagery using wavelet spectral analysis

Hyperspectral imagery provides richer information about materials than multispectral imagery. The new larger data volumes from hyperspectral sensors present a challenge for traditional processing techniques. For example, the identification of each ground surface pixel by its corresponding spectral signature is still difficult because of the immense volume of data. Conventional classification methods may not be used without dimension reduction preprocessing. This is due to the curse of dimensionality, which refers to the fact that the sample size needed to estimate a function of several variables to a given degree of accuracy grows exponentially with the number of variables. Principal component analysis (PCA) has been the technique of choice for dimension reduction. However, PCA is computationally expensive and does not eliminate anomalies that can be seen at one arbitrary band. Spectral data reduction using automatic wavelet decomposition could be useful. This is because it preserves the distinctions among spectral signatures. It is also computed in automatic fashion and can filter data anomalies. This is due to the intrinsic properties of wavelet transforms that preserves high- and low-frequency features, therefore preserving peaks and valleys found in typical spectra. Compared to PCA, for the same level of data reduction, we show that automatic wavelet reduction yields better or comparable classification accuracy for hyperspectral data, while achieving substantial computational savings.

An automated parallel image registration technique based on the correlation of wavelet features

With the increasing importance of multiple multiplatform remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Our work utilizes maxima of wavelet coefficients to form the basic features of a correlation-based automatic registration algorithm. Our wavelet-based registration algorithm is tested successfully with data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Landsat Thematic Mapper (TM), which differ by translation and/or rotation. By the choice of high-frequency wavelet features, this method is similar to an edge-based correlation method, but by exploiting the multiresolution nature of a wavelet decomposition, our method achieves higher computational speeds for comparable accuracies. This algorithm has been implemented on a single-instruction multiple-data (SIMD) massively parallel computer, the MasPar MP-2, as well as on the CrayT3D, the Cray T3E, and a Beowulf cluster of Pentium workstations.

Refining image segmentation by integration of edge and region data

A basic requirement for understanding the dynamics of the Earths major ecosystems is accurate quantitative information about the distribution and areal extent of the Earths vegetation formations. Some of this required information can be obtained through the analysis of remotely sensed data. Image segmentation is often one of the first steps of this analysis. This paper focuses on two particular types of segmentation: region-based and edge-based segmentations. Each approach is affected differently by various factors, and both types of segmentations may be improved by taking advantage of their complementary nature. Included among region-based segmentation approaches are region growing methods, which produce hierarchical segmentations of images from finer to coarser resolution. In this hierarchy, an ideal segmentation (ideal for a given application) does not always correspond to one single iteration, but map correspond to several different iterations. This, among other factors, makes it somewhat difficult to choose a stopping criterion for region growing methods. To find the ideal segmentation, the authors develop a stopping criterion for their Iterative Parallel Region Growing (IPRG) algorithm using additional information from edge features, and the Hausdorff distance metric. They integrate information from regions and edges at the symbol level, taking advantage of the hierarchical structure of the region segmentation results. Also, to demonstrate the feasibility of this approach in processing the massive amount of data that will be generated by future Earth remote sensing missions, such as the Earth Observing System (EOS), all the different steps of this algorithm have been implemented on a massively parallel processor. >

The translation sensitivity of wavelet-based registration

This paper studies the effects of image translation on wavelet-based image registration. The main result is that the normalized correlation coefficients of low-pass Haar and Daubechies wavelet subbands are essentially insensitive to translations for features larger than twice the wavelet blocksize. The third-level low-pass subbands produce a correlation peak that varies with translation from 0.7 and 1.0 with an average in excess of 0.9. Translation sensitivity is limited to the high-pass subband and even this subband is potentially useful. The correlation peak for high-pass subbands derived from first and second-level low-pass subbands ranges from about 0.0 to 1.0 with an average of about 0.5 for Daubechies and 0.7 for Haar. We use a mathematical model to develop these results, and confirm them on real data.

Wavelet spectral dimension reduction of hyperspectral imagery on a reconfigurable computer

Hyperspectral imagery, by definition, provides valuable remote sensing observations at hundreds of frequency bands. Conventional image classification (interpretation) methods may not be used without dimension reduction preprocessing. Automatic wavelet reduction has been proven to yield better or comparable classification accuracy, while achieving substantial computational savings. However, the large hyperspectral data volumes remain to present a challenge for traditional processing techniques. Reconfigurable computers (RCs) can leverage the synergism between conventional processors and FPGAs to provide low-level hardware functionality at the same level of programmability as general-purpose computers. We investigate the potential of using RCs for on-board, i.e. aboard airborne/spaceborne carriers, preprocessing of hyperspectral imagery by prototyping for the first time the automatic wavelet dimension reduction algorithm. Our investigation exploits the fine and coarse grain parallelism provided by the RCs and has been experimentally verified on one of the state-of the art reconfigurable platforms, SRC-6E. An order of magnitude speedup over traditional processing techniques has been reported.

First evaluation of automatic image registration methods

As the need for automating registration techniques is recognized, the authors feel that there is a need to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. The authors present the first steps towards this quantitative evaluation: a few automatic image registration algorithms are described and first results of their evaluation are presented for three different datasets.

Automatic Extraction of Ellipsoidal Features for Planetary Image Registration

With the launch of several planetary missions in the last decade, a large amount of planetary images has been already acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Here, we propose a new unsupervised method for the extraction of different features of elliptical and geometrically compact shapes, such as craters and rocks of compact shape (e.g., boulders), to be used for image registration purposes. This approach is based on the combination of several image processing techniques, including watershed segmentation and the generalized Hough transform. The method potentially has application for extraction of craters, rocks, and other geological features.

A new approach to image fusion based on cokriging

We consider the image fusion problem involving remotely sensed data. We introduce cokriging as a method to perform fusion. We investigate the advantages of fusing Hyperion with ALI. This evaluation is performed by comparing the classification of the fused data with that of input images and by calculating well-chosen quantitative fusion quality metrics. We consider the invasive species forecasting system (ISFS) project as our fusion application. The fusion of ALI with Hyperion data is studied using PCA and wavelet-based fusion. We then propose utilizing a geostatistical based interpolation method called cokriging as a new approach for image fusion.

Automatic Extraction of Planetary Image Features

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of features from the Lunar surface, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration, and can be generalized to other planetary images as well.

Research issues in image registration for remote sensing

Image registration is an important element in data processing for remote sensing with many applications and a wide range of solutions. Despite considerable investigation the field has not settled on a definitive solution for most applications and a number of questions remain open. This article looks at selected research issues by surveying the experience of operational satellite teams, application-specific requirements for Earth science, and our experiments in the evaluation of image registration algorithms with emphasis on the comparison of algorithms for subpixel accuracy. We conclude that remote sensing applications put particular demands on image registration algorithms to take into account domain-specific knowledge of geometric transformations and image content.