Multi-Sensor Image Registration, Fusion and Dimension Reduction

With the development of future spacecraft formations comes a number of complex challenges such as maintaining precise relative position and specified attitudes, as well as being able to communicate with each other. More generally, with the advent of spacecraft formations, issues related to performing on-board and automatic data computing and analysis as well as decision planning and scheduling will figure among the most important requirements. Among those, automatic image registration, image fusion and dimension reduction represent intelligent technologies that would reduce mission costs,would enable autonomous decisions to be taken on-board, and would make formation flying adaptive, self-reliant, and cooperative. For both on-board and on-the-ground applications, the particular need for dimension reduction is two-fold, first to reduce the communication bandwidth, second as a pre-processing to make computations feasible,simpler and faster

Shearlet Features for Registration of Remotely Sensed Multitemporal Images

We investigate the role of anisotropic feature extraction methods for automatic image registration of remotely sensed multitemporal images. Building on the classical use of wavelets in image registration, we develop an algorithm based on shearlets, a mathematical generalization of wavelets that offers increased directional sensitivity. Experimental results on multitemporal Landsat images are presented, which indicate superior performance of the shearlet algorithm when compared to classical wavelet algorithms

Introduction to Remote Sensing Image Registration

For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their application

Distributed Spacecraft Missions (DSM) Technology Development at NASA Goddard Space Flight Center

For the last 5 years, NASA Goddard has been investigating Distributed Spacecraft Missions (DSM) system architectures, surveying past, current and potential mission concepts, developing several taxonomies and identifying some key technologies that will enable future DSM mission design, development, operations and management. This paper summarizes this Initiative and the talk will provide details about specific Goddard DSM projects that are currently underway and that are relevant to future Earth Science missions

Introduction to NASA Goddard Workshop on Artificial Intelligence

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few.This workshop will be investigating how AI technologies can be adapted or developed to address the following challenges: Discover events of interest and correlations in large amounts of science data; improve the outcomes of science modeling and data assimilation using improved data processing, integration, and analysis. Design advisors for mission planning and operations, including anomaly detection and spacecraft health monitoring. Develop tools for engineering support, including advanced manufacturing, orbit determination, new component design and system engineering. Customize intelligent user interfaces, including visual analytics and natural language processing

Overview of Artificial Intelligence (AI) at NASA Goddard

Artificial Intelligence (AI) is a collection of advanced technologies that allows machines to think and act, both humanly and rationally, through sensing, comprehending, acting and learning. AI's foundations lie at the intersection of several traditional fields Philosophy, Mathematics, Economics, Neuroscience, Psychology and Computer Science. Although the inception of AI started in the 1950's, it has recently made a strong comeback in all aspects of society and all over the world; this is mainly due to the timely combination of increased data volumes, advanced and mature algorithms, and improvements in computing power and storage. Current AI applications include big data analytics, robotics, intelligent sensing, assisted decision making, and speech recognition just to name a few. During the Tour, we will show a few examples of the current AI activities at NASA Goddard

Instrument Modeling Concepts for Tradespace Analysis of Satellite Constellations

Constellations are gaining popularity in government and commercial space-based missions for Earth Observation (EO) due to their risk tolerance and ability to improve observation sampling in space and time. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the concept of modeling the primary science instruments within TAT-C, using a radar as an example, but extendable to imagers, occulters and lidars. The modularity of TAT-C's software architecture allows for crisply defining the interface between TAT-C's user defined or internal variables and the payload variables. The described module will inform TAT-C users of payload-dependent performance differences among thousands of constellation architectures (e.g. revisit time of the sensor swath, differential signal to noise ratio (SNR), spatial resolution of measurements) and allow them to pick an appropriate constellation architecture for detailed development. The module may also inform operational decisions of satellite modes, based on ground optimization or onboard autonomy

IMAGESEER - IMAGEs for Education and Research

IMAGESEER is a new Web portal that brings easy access to NASA image data for non-NASA researchers, educators, and students. The IMAGESEER Web site and database are specifically designed to be utilized by the university community, to enable teaching image processing (IP) techniques on NASA data, as well as to provide reference benchmark data to validate new IP algorithms. Along with the data and a Web user interface front-end, basic knowledge of the application domains, benchmark information, and specific NASA IP challenges (or case studies) are provided

Improving Imaging Instrument Spatial Resolution Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image

Improving the Spatial Resolution of Imaging Instruments Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image