Feature Reduction using a Singular Value Decomposition for the Iterative Guided Spectral Class Rejection Hybrid Classifier

Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This work introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/nonforest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVDbased feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondonia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVDbased feature reduction can produce statistically significantly better classifications than PCA

An Adaptive Noise Filtering Algorithm for AVIRIS Data with Implications for Classification Accuracy

This paper describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into “bins” with other bands having similar SNRs. A median filter with a variable sized kernel is then applied to each band, with the same size kernel used for each band in a particular bin. The proposed adaptive filters are applied to a hyperspectral image generated by the AVIRIS sensor, and results are given for the identification of three different pine species located within the study area. The adaptive filtering scheme improves image quality as shown by estimated SNRs, and classification accuracies improved by more than 10% on the sample study area, indicating that the proposed methods improve the image quality, thereby aiding in species discrimination

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region

Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms