Nargess Memarsadeghi

A FAST IMPLEMENTATION OF THE ISODATA CLUSTERING ALGORITHM

Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.

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.

Efficient Kriging via Fast Matrix-Vector Products

Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrix-vector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.

Developing wide-field spatio-spectral interferometry for far-infrared space applications

Interferometry is an affordable way to bring the benefits of high resolution to space far-IR astrophysics. We summarize an ongoing effort to develop and learn the practical limitations of an interferometric technique that will enable the acquisition of high-resolution far-IR integral field spectroscopic data with a single instrument in a future space-based interferometer. This technique was central to the Space Infrared Interferometric Telescope (SPIRIT) and Submillimeter Probe of the Evolution of Cosmic Structure (SPECS) space mission design concepts, and it will first be used on the Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII). Our experimental approach combines data from a laboratory optical interferometer (the Wide-field Imaging Interferometry Testbed, WIIT), computational optical system modeling, and spatio-spectral synthesis algorithm development. We summarize recent experimental results and future plans.

Image Fusion Using Cokriging

Scientists in various disciplines are faced with huge amounts of data that need to be studied and analyzed. NASA alone has around 18 satellites with over 80 sensors, all of which send a tremendous amount of data from around the globe continuously. An important step in modern data processing applications where data are gathered from multiple sources is data fusion. Data fusion is defined as the process of dealing with information from multiple sources to achieve refined and improved information for decision making [1]. Image fusion is a subset of the general data fusion problem where data being fused are images. The goal of performing image fusion is usually to increase either the spatial or spectral resolution of images involved. One particular case of image fusion is pan-sharpening. Pansharpening is a technique which deals with the limitations of sensors in capturing high resolution multispectal (MS) images [2]. That is panchromatic (Pan) images have high spatial resolution and low spectral resolution. On the other hand, MS images have high spectral resolution, since they cover a narrower wavelength range, but have a lower spatial resolution. Image fusion is then used as a tool to create a high spatial and spectral resolution image given Pan and MS images. In this paper we show how to apply fusion for the purpose of pan-sharpening multispectral Landsat ETM bands by using cokriging. We employ the cokriging interpolation method for image fusion of remotely sensed data [3], [4]. In particular, we show preliminary results on applying a variant called ordinary cokriging for pan-sharpening of multispectral images from the Landsat 7 sensor. We initially proposed cokriging for image fusion in [5] and showed preliminary results on increasing the spectral resolution of ALI using Hyperion. In this paper, we address the problem of increasing the spatial resolution of multispectral bands of a sensor using a panchromatic image through. We then evaluate both spectral and spatial quality of our fused images through few quantitative measures. We also compare our results to those obtained from more traditional approaches based on principal component analysis and wavelets.

Image Registration and Fusion Studies for the Integration of Multiple Remote Sensing Data

The future of remote sensing will see the development of spacecraft formations, and with this development will come a number of complex challenges such as maintaining precise relative position and specified attitudes. At the same time, there will be increasing needs to understand planetary system processes and build accurate prediction models. One essential technology to accomplish these goals is the integration of multiple source data. For this integration, image registration and fusion represent the first steps and need to be performed with very high accuracy. In this paper, we describe studies performed in both image registration and fusion, including a modular framework that was built to describe registration algorithms, a Web-based image registration toolbox, and the comparison of several image fusion techniques using data from the EO-1/ALI and Hyperion sensors

The Wide-Field Imaging Interferometry Testbed (WIIT): Recent Progress and Results

Research with the Wide-Field Imaging Interferometry Testbed (WIIT) is ongoing, and in the past year we have achieved several important milestones. We have moved WIIT into the Advanced Interferometry and Metrology (AIM) Laboratory at Goddard, and have characterized the testbed in this well-controlled environment. The system is now completely automated and we are in the process of acquiring large data sets for analysis. In this paper, we discuss these new developments and outline our future research directions. The WIIT testbed, combined with new data analysis techniques and algorithms, provides a demonstration of the technique of wide-field interferometric imaging, a powerful tool for future space-borne interferometers. Algorithm development is discussed in a separate paper within this conference.

Moon Search Algorithms for NASA's Dawn Mission to Asteroid Vesta

A moon or natural satellite is a celestial body that orbits a planetary body such as a planet, dwarf planet, or an asteroid. Scientists seek understanding the origin and evolution of our solar system by studying moons of these bodies. Additionally, searches for satellites of planetary bodies can be important to protect the safety of a spacecraft as it approaches or orbits a planetary body. If a satellite of a celestial body is found, the mass of that body can also be calculated once its orbit is determined. Ensuring the Dawn spacecrafts safety on its mission to the asteroid (4) Vesta primarily motivated the work of Dawns Satellite Working Group (SWG) in summer of 2011. Dawn mission scientists and engineers utilized various computational tools and techniques for Vestas satellite search. The objectives of this paper are to 1) introduce the natural satellite search problem, 2) present the computational challenges, approaches, and tools used when addressing this problem, and 3) describe applications of various image processing and computational algorithms for performing satellite searches to the electronic imaging and computer science community. Furthermore, we hope that this communication would enable Dawn mission scientists to improve their satellite search algorithms and tools and be better prepared for performing the same investigation in 2015, when the spacecraft is scheduled to approach and orbit the dwarf planet (1) Ceres.

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

Interpolating scattered data points is a problem of wide ranging interest. One of the most popular interpolation methods in geostatistics is ordinary kriging. The price for its statistical optimality is that the estimator is computationally very expensive. We demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods.

Citizen Science [Guest editors' introduction]

In this introduction for the special issue on citizen science, the guest editor describes what is meant by citizen science and crowdsourcing projects as well as what their importance and impacts are. She also introduces some of the projects briefly and provides resources for accessing and learning more about citizen science projects.