Publications by J. Love

Detection and correction of abnormal pixels in hyperion images

Fri, 01 Nov 2002

Hyperion images are currently processed to level 1a (from level 0 or raw data). These level 1a images are files o radiometrically corrected datain units of either watts/(sr x micron x m2) x 40 for VNIR bands or watts/(sr x micron x m2) x 80 for SWIR bands. Each distributed Hyperion level 1a image tape contains a log file, called "(E)-1 identifier).fix.log", that reports the bad or corrupted pixels (called known bad pixels) found during the pre-flight checking, and details how they were fixed. All bad pixels should be corrected in a level 1a image. However, bad pixels are still evident. In addition, there are dark vertical stripes in the image that are not reported in the log file. In this paper, we introduce a method to detect and correct the bad pixels and vertical stripes (we will refer to theses occurrences as abnormal pixels). Images from the Great Victoria Watershed and other EVEOSD test sites are used to determine how stationary the locations of the abnormal pixels are. After abnormal pixel correction a Hyperion image is ready for geometric correction, atmospheric correctiion, and further analysis

Automated methods for atmospheric correction and fusion of multispectral satellite data for national monitoring

Mon, 26 Aug 2002

The Earth Observation for Sustainable Development of Canada's forests (EOSD) project monitors Canada's forests from space. Canada contains ten-percent of the world's forests. Initial EOSD products are land cover, forest change, forest biomass, and automated methods. There are more than 500 LANDSAT TM or ETM+ scenes required for a single coverage of Canada's forests. Multi-temporal analysis using satellite data requires automation for conversion of these data to common units of exoatmospheric radiance or ground reflectance. During the next ten years the EOSD project will use a variety of Landsat optical and Radarsat sensors. A diverse set of ancillary and satellite data formats exist which require the development of adaptable data ingest and processing streams. Legacy LANDSAT TM and ETM+ data are available in a number of different formats from several national and US suppliers. In this paper, we present an automated system for managing processing streams for calibration and atmospheric correction of LANDSAT TM and ETM+ data to create data sets ready to analyze for EOSD products. Using known forest attributes from GIS data and field measurements, we validated our results of studies undertaken to assess spectral signal variability using both at sensor radiance and ground reflectance for LANDSAT TM and ETM+ for a test site on Vancouver Island, BC. We present a strategy for correcting and fusing multi-source and multitemporal satellite data for meeting EOSD requirements.

Monitoring Forests with Hyperion and ALI

Mon, 26 Aug 2002

Hyperion, a hyperspectral sensor, and the Advanced Land Imager (ALI) are carried on NASA’s EO-1 satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission. With 10% of the world’s forests and the second largest country by area in the world, Canada has a natural requirement for effective monitoring of its forests. Eight test sites have been selected for EVEOSD, with seven in Canada and one in the US. Extensive fieldwork has been conducted at four of these sites.
A comparison is made of forest classification results from Hyperion, ALI, and the ETM+ of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and ortho-rectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 220 channels to 12 features. Classes chosen for discrimination included Douglas Fir, Hemlock, Western Red Cedar, Lodgepole Pine and Red Alder. Overall classification accuracies obtained for each sensor were: Hyperion 92.9%, ALI 84.8%, and ETM+ 75.0%. Hyperspectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest forests in comparison to Landsat-7.

Geometric Correction and Validation of Hyperion and ALI Data for EVEOSD

Mon, 26 Aug 2002

Precise geometric correction of EO-1’s Hyperion data is essential to link ground spectral data and satellite hyperspectral data. Two scenes have been selected from sites of the EVEOSD (Evaluation and Validation of EO-1 for Sustainable Development of Forests) project. One site is the Greater Victoria Watershed District (GVWD) located on south Vancouver Island, BC and the other is Hoquiam located in southwestern Washington State. Ground Control Point (GCP) collection has been performed using a feature fitting method in which high accuracy, orthorectified photo-derived polygons of features are used for tie-down. For example lakes are adjusted to match the same feature obvious in the hyperspectral imagery. This technique allows for easier estimation of a GCP’s precise fit to the imagery. A third (11) of the GCPs were identified as check points to validate the geometric models. GCPs were collected independently from both the VNIR and SWIR arrays of the Hyperion sensor to determine the adjustment factor required to remove the displacement and skew between these arrays. The adjustment can then be applied to GCPs collected from one array to make a compatible geometric correction model for both arrays. The polynomial and rational function correction methods have been applied to both scenes with various orders applied to each function. The effect of terrain distortion removal is evaluated in using the rational function method. Hyperion data can be geocorrected with surprising accuracy. For example, we obtained 10 m RMS on check points with the rational function. With a second order polynomial we achieved 13 m RMS without terrain correction. The accuracy of this latter result is due to the small swath width of the sensor. Applying terrain correction does improve the accuracy of geometric correction in areas with high relief. A similar procedure was applied to EO-1’s ALI sensor and this paper compares the results for Hyperion and ALI geometric fidelity.