Name
Title | ||
|---|---|---|
Noise Estimation of Remote Sensing Reflectance Using a Segmentation Approach Suitable for Optically Shallow Waters |
Abstract | ||
|---|---|---|
This paper outlines a methodology for the estimation of the environmental noise equivalent reflectance in aquatic remote sensing imagery using an object-based segmentation approach. Noise characteristics of remote sensing imagery directly influence the accuracy of estimated environmental variables and provide a framework for a range of sensitivity, sensor specification, and algorithm design studies. The proposed method enables estimation of the noise equivalent reflectance covariance of remote sensing imagery through homogeneity characterization using image segmentation. The method is first tested on a synthetic data set with known noise characteristics and is successful in estimating the noise equivalent reflectance under a range of segmentation structures. Testing on a Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral image in a coral reef environment shows the method to produce comparable noise equivalent reflectance estimates in an optically shallow water environment to those previously derived in optically deep water. This method is of benefit in aquatic studies where homogenous regions of optically deep water were previously required for image noise estimation. The ability of the method to characterize the covariance of an image is of significant benefit when developing probabilistic inversion techniques for remote sensing. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TGRS.2014.2313129 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
coral reef environment,remote sensing,optically shallow water environment,noise covariance,object-based segmentation approach,noise characteristics,segmentation approach,aquatic remote sensing imagery,environmental noise equivalent reflectance,image segmentation,phills hyperspectral image,aquatic remote sensing,synthetic data set,portable hyperspectral imager for low-light spectroscopy,remote sensing imagery,geophysical image processing,remote sensing reflectance noise estimation,optically shallow waters,segmentation structures,oceanographic techniques,optically deep water,noise,optical imaging,estimation | Computer vision,Segmentation,Remote sensing,Hyperspectral imaging,Image noise,Image segmentation,Synthetic data,Artificial intelligence,Probabilistic logic,Environmental noise,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
52 | 12 | 0196-2892 |
Citations | PageRank | References |
2 | 0.38 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Sagar | 1 | 2 | 1.05 |
| Vittorio E. Brando | 2 | 82 | 18.41 |
| Malcolm Sambridge | 3 | 2 | 0.38 |