Roger L. King

An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets

High correlation among the neighboring pixels both spatially and spectrally in a multispectral image makes it necessary to use an efficient data transformation approach before performing pan-sharpening. Wavelets and principal component analysis (PCA) methods have been a popular choice for spatial and spectral transformations, respectively. Current PCA-based pan-sharpening methods make an assumption that the first principal component (PC) of high variance is an ideal choice for replacing or injecting it with high spatial details from the high-resolution histogram-matched panchromatic (PAN) image. This paper presents a combined adaptive PCA-contourlet approach for pan-sharpening, where the adaptive PCA is used to reduce the spectral distortion and the use of nonsubsampled contourlets for spatial transformation in pan-sharpening is incorporated to overcome the limitation of the wavelets in representing the directional information efficiently and capturing intrinsic geometrical structures of the objects. The efficiency of the presented method is tested by performing pan-sharpening of the high-resolution (IKONOS and QuickBird) and the medium-resolution (Landsat-7 Enhanced Thematic Mapper Plus) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive PCA approach helps reducing the spectral distortion, and its merger with contourlets provides better fusion results.

High Performance Computing for Hyperspectral Remote Sensing

Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.

Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion

The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels

Statistical Estimation of Daily Maximum and Minimum Air Temperatures from MODIS LST Data over the State of Mississippi

Recent studies have shown that the Land Surface Temperature (LST) data measured by Moderate Resolution Imaging Spectro-Radiometer (MODIS) from both the Terra and Aqua platforms can be successfully used for linear regression estimates of daily maximum and minimum air temperatures at a local scale. Incorporation of these estimates into spatial interpolation schemes results in accuracy improvement of the surface air temperature, provided that the correlation coefficient (R) between the air temperature and LST is rather high. The purpose of this work was to examine the importance of pixel resolution (1.0 and 5.0 km2), satellite overpass time, season, land cover type, and the vegetation fraction (depending on the view zenith angle of the MODIS instrument) in controlling the observed level of R. The relative contribution of these factors in producing R variations has been assessed over the state of Mississippi during 2000-2004. Similarly, the sensitivity analysis of the difference between daily maximum and minimum air temperatures and LST to the same factors was performed. Results from these analyses have shown that R and the average difference between temperatures exhibited rather consistent variations depending on the above factors. The difference between maximum air temperature and LST increased linearly with the view angle (having typical range of 1-2°C for angle changes from 0° to ±65°) and remained constant or slightly decreased for daily air minimum temperatures. Both Terra and Aqua 1.0 km2 LST exhibited a small but persistent increase of R between the air temperature and LST as compared with that of using 5.0 km2 LST. Changing from Terra to Aqua LST did not alter significantly estimated values of R. This result suggested that the time difference between the moment of the satellite overpass and the time when maximum or minimum air temperature was observed was not critical for controlling the R value between the air temperature and LST at the involved spatial scales.

On the Performance Evaluation of Pan-Sharpening Techniques

The limitations of the currently existing pan-sharpening quality indices are analyzed: the absolute difference between pixel values, mean shifting, and dynamic range change is frequently used as spatial fidelity measurement, but they may not correlate well with the actual change of image content; and spectral angle is a widely used metric for spectral fidelity, but the spectral angle remains the same if two vectors are multiplied by two individual constants, which means the average spectral angle between two multispectal images is zero even if pixel vectors are multiplied by different constants. Therefore, it is important to evaluate the quality of a pan-sharpened image under a task of its practical use and to assess spectral fidelity in the context of an image. In this letter, three data analysis techniques in linear unmixing, detection, and classification are applied to evaluate spectral information within a spatial scene context. It is demonstrated that those old but simplest approaches, i.e., Brovey and multiplicative (or after straightforward adjustment) methods, can generally yield acceptable data analysis results. Thus, it is necessary to consider the tradeoff between computational complexity, actual improvement on application, and hardware implementation when developing a pan-sharpening method.

End-member extraction for hyperspectral image analysis

We investigate the relationship among several popular end-member extraction algorithms, including N-FINDR, the simplex growing algorithm (SGA), vertex component analysis (VCA), automatic target generation process (ATGP), and fully constrained least squares linear unmixing (FCLSLU). We analyze the fundamental equivalence in the searching criteria of the simplex volume maximization and pixel spectral signature similarity employed by these algorithms. We point out that their performance discrepancy comes mainly from the use of a dimensionality reduction process, a parallel or sequential implementation mode, or the imposition of certain constraints. Instructive recommendations in algorithm selection for practical applications are provided.

A wavelet based algorithm for pan sharpening Landsat 7 imagery

At IGARSS 2000 the Data Fusion Committee began the sponsorship of a series of contests with which the committee aimed to reach two goals. The first goal was to assess the state of the art of a clearly defined aspect of the data fusion field by empirically evaluating algorithms. The second goal was to identify collective weaknesses of current algorithms, so as to identify requirements of further research. The first contest focused in the sharpening of 30-m resolution multispectral images by using 15-m panchromatic imagery. The objective of the sharpening was to improve the spatial resolution of the multispectral imagery, while preserving the spectral information in homogeneous areas. For the IGARSS 2000 contest, Landsat 7 Thematic Mapper data acquired over urban and agricultural areas was used. Researchers from the Remote Sensing Technologies Center at Mississippi State University won the contest. This paper describes the pan sharpening methodology used.

Semantics-enabled framework for knowledge discovery from Earth observation data archives

Earth observation data have increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of 3 TB of data a day. This data acquisition rate is a major challenge to the existing data exploitation and dissemination approaches. The lack of content- and semantic-based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. In this paper, we describe a framework we have developed [Intelligent Interactive Image Knowledge Retrieval (I/sup 3/KR)] that is built around a concept-based model using domain-dependant ontologies. In this framework, the basic concepts of the domain are identified first and generalized later, depending upon the level of reasoning required for executing a particular query. We employ an unsupervised segmentation algorithm to extract homogeneous regions and calculate primitive descriptors for each region based on color, texture, and shape. We initially perform an unsupervised classification by means of a kernel principal components analysis method, which extracts components of features that are nonlinearly related to the input variables, followed by a support vector machine classification to generate models for the object classes. The assignment of concepts in the ontology to the objects is achieved automatically by the integration of a description logics-based inference mechanism, which processes the interrelationships between the properties held in the specific concepts of the domain ontology. The framework is exercised in a coastal zone domain.

Information services for smart grids

Interconnected and integrated electrical power systems, by their very dynamic nature are complex. These multifaceted systems are subject to a host of challenges - aging infrastructure, generation availability near load centers, transmission expansion to meet growing demands, distributed resources, dynamic reactive compensation, congestion management, grid ownership vs. system operation, reliability coordination, supply and cost of natural resources for generation, etc. Other types of challenges facing the industry today include balancing between resource adequacy, reliability, economics, environmental constraints, and other public purpose objectives to optimize transmission and distribution resources to meet the needs of the end users. The goal is to provide a vision for a comprehensive and systematic approach to meeting the grid management challenges through new information services. These services will have as the heart of their data streams a sensor web enablement that will make the grid a part of the semantic Web.

A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico

Harmful algal blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting system for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. We propose a machine learning based spatio-temporal data mining approach for the detection of HAB events in the region of the Gulf of Mexico. In this study, a spatio-temporal cubical neighborhood around the training sample is introduced to retrieve relevant spectral information of both HAB and non-HAB classes. The feature relevance is studied through mutual information criterion to understand the important features in classifying HABs from non-HABs. Kernel based support vector machine is used as a classifier in the detection of HABs. This approach gives a significant performance improvement by reducing the false alarm rate. Further, with the achieved classification accuracy, the seasonal variations and sequential occurrence of algal blooms are predicted from spatio-temporal datasets. New variability visualization is introduced to illustrate the dynamic behavior of HABs across space and time.