Paul A. Pope

Satellite-based columnar water vapor retrieval with the multi-spectral thermal imager (MTI)

The Multi-spectral Thermal Imager (MTI) has three near-infrared bands (E, F, and G) within the 850-1050-nm spectral range that are used for the columnar water vapor (CWV) retrieval using the continuum interpolated band ratio (CIBR) and the atmospheric precorrected differential absorption (APDA) methods. The retrieved CWV amounts are compared with the aerosol robotic network (AERONET) measurements at the Oklahoma Atmospheric Radiation Measurement (ARM) program and the Stennis Space Center sites. We find no significant difference in the accuracy of the two tested methods. However, there is a considerable difference in the root mean square error (RMSE) for the CWV retrieval over the Oklahoma ARM and the Stennis Space Center sites. The overall RMSE of the MTI CWV retrieval is found to be 13% to 14%. The error is reduced to 11% to 12% for CWV amounts larger then 1 g/cm/sup 2/.

Feature extraction from multiple data sources using genetic programming

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor (MAMS) imagery

High resolution (100 m), sequential multispectral atmospheric mapping sensor (MAMS) images were used to calculate sea surface velocities from the advection of visible and thermal surface features using maximum cross correlation (MCC) and subjective techniques. The visible gradient image velocities agreed well with the subjective motion, while the infrared channel performed best without computing gradients. >

LANL experience with coregistration of MTI imagery

The fifteen-channel Multispectral Thermal Imager (MTI) provides accurately calibrated satellite imagery for a variety of scientific and programmatic purposes. To be useful, the calibrated pixels from the individual detectors on the focal plane of this pushbroom sensor must be resampled to a regular grid corresponding to the observed scene on the ground. In the LEVEL1B_R_COREG product, it is required that the pixels from different spectral bands and from different sensor chip assemblies all be coregistered to the same grid. For the LEVEL1B_R_GEO product, it is further required that this grid be georeferenced to the Universal Transverse Mercator coordinate system. It is important that an accurate registration is achieved, because most of the higher level products (e.g. ground reflectance) are derived from these LEVEL1B_R products. Initially, a single direct georeferencing approach was pursued for performing the coregistration task. Although this continues to be the primary algorithm for our automated pipeline registration, we found it advantageous to pursue alternative approaches as well. This paper surveys these approaches, and offers lessons learned during the three years we have been addressing the coregistration requirements for MTI imagery at the Los Alamos National Laboratory (LANL).

MTI science, data products and ground data processing overview

The mission of the Multispectral Thermal Imager (MTI) satellite is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of urban and industrial areas, as well as sites of environmental interest. The satellite makes top-of-atmosphere radiance measurements that are subsequently processed into estimates of surface properties such as vegetation health, temperatures, material composition and others. The MTI satellite also provides simultaneous data for atmospheric characterization at high spatial resolution. To utilize these data the MTI science program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data interpretation and analysis. Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and programmability. In addition, the MTI science team has produced detailed site, system and atmospheric models to aid in system design and data analysis. This paper provides an overview of the MTI research objectives, data products and ground data processing.

On the verification and validation of geospatial image analysis algorithms

Verification and validation (V&V) of geospatial image analysis algorithms is a difficult task and is becoming increasingly important. While there are many types of image analysis algorithms, we focus on developing V&V methodologies for algorithms designed to provide textual descriptions of geospatial imagery. In this paper, we present a novel methodological basis for V&V that employs a domain-specific ontology, which provides a naming convention for a domain-bounded set of objects and a set of named relationships between these objects. We describe a validation process that proceeds through objectively comparing benchmark imagery, produced using the ontology, with algorithm results. As an example, we describe how the proposed V&V methodology would be applied to algorithms designed to provide textual descriptions of facilities.

Investigation of automated feature extraction using multiple data sources

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use of multiple data sources for various feature extraction tasks. We compare and contrast this softwares capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

Evolving feature extraction algorithms for hyperspectral and fused imagery

Hyperspectral imagery with moderate spatial resolution (/spl sim/30 m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatio-spectral feature extraction pipelines from training data provided via an intuitive, point-and-click interface. We describe recent work exploring geospatial feature extraction from hyperspectral imagery, and from a multi-instrument fused dataset. For hyperspectral imagery, we demonstrate our system on NASA Earth Observer 1 (EO-1) Hyperion imagery, applied to agricultural crop detection. We present an evolved pipeline, and discuss its operation. We also discuss work with multi-spectral imagery (DOE/NNSA Multispectral Thermal Imager) fused with USGS digital elevation model (DEM) data, with the application of detecting mixed conifer forest.

Automated simultaneous multiple feature classification of MTI data

Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.

Design of benchmark imagery for validating facility annotation algorithms

The design of benchmark imagery for validation of image annotation algorithms is considered. Emphasis is placed on imagery that contains industrial facilities, such as chemical refineries. An application-level facility ontology is used as a means to define salient objects in the benchmark imagery. In-strinsic and extrinsic scene factors important for comprehensive validation are listed, and variability in the benchmarks discussed. Finally, the pros and cons of three forms of benchmark imagery: real, composite and synthetic, are delineated.