Ratio-based bottom depth-retrieval algorithms are conceptually simple relative to other algorithms and can be effective. The objective of this study was to determine the utility of imposing a spatial-smoothing assumption on three ratio-based, feed-forward remote-sensing bathymetry algorithms: Polcyn et al., Stumpf et al., and Dierssen et al. We consider three smoothing operators: median, Savitzky-Golay, and linear diffusion with data fidelity, applied in three domains: spatial, spectral, and spectral-spatial. Thus, we consider nine smoothing methods. In addition, we consider two points at which smoothing is applied: one before the inversion process (pre-smoothing) and the other after the inversion process (post-smoothing). Our new formulations were tested with synthetic data, in situ remote-sensing reflectance, and simultaneous acoustic bathymetry, acquired in optically shallow waters. Analysis and results from the synthetic-data experiment indicate that pre-smoothing method is more effective than post-smoothing method. The field-data experiments indicate that spatial-domain smoothing is effective regardless of the type of smoothing operator, whereas spectral smoothing is not. Spectral-spatial-domain smoothing is as effective as spatial-domain smoothing, but is prone to over-segmentation. Effectiveness of spatial pre-smoothing was observed with every ratio-based inversion method, which suggests potential universal applicability of smoothing operators to ratio-based algorithms.
The Cerrado is a savanna ecoregion with grassland and woodland subtypes covering ~one-quarter of Brazil and is
considered to be a biodiversity hotspot, threatened by land-use conversion. Hyperspectral remote sensing enables spatio-temporal
monitoring, while providing the possibility of vegetation-mapping at a high level of specificity. However,
because imaging spectrometer data availability/coverage is currently limited, a need exists for effective exploitation of
multispectral satellite imagery with broad-area spatial coverage. The objective was to assess the utility of hyperspectral
Hyperion and multispectral CBERS-2 satellite imagery in discriminating among Cerrado subtypes and agricultural
classes. Temporally-coincident field-transect data for Cerrado physiognomies and agricultural sites were collected,
including biophysical metrics. Nonmetric multidimensional scaling and hierarchical cluster analysis were used to
identify potential environmental gradients of biophysical groupings. Four Cerrado subclasses were identified: Campo
Limpo (Open Cerrado Grassland), Campo Sujo (Shrub Savanna), Cerrado Típico (Wooded Cerrado), and Cerrado Denso
(Cerrado Woodland). Subclasses were then merged, forming two Cerrado subclasses. To facilitate sensor
intercomparison, image classification involved PCA transformations, followed by unsupervised clustering of the
component images. Results indicate that both dimensionality-reduced Hyperion and CBERS datasets were sufficient in
distinguishing between the two more general Cerrado subclasses and agriculture, but the Hyperion-derived classification
was more accurate.
The United States Navy has recently shifted focus from open-ocean warfare to joint operations in optically complex nearshore regions. Accurately estimating bathymetry and water column inherent optical properties (IOPs) from passive remotely sensed imagery can be an important facilitator of naval operations. Lee et al. developed a semianalytical model that describes the relationship between shallow-water bottom depth, IOPs and subsurface and above-surface reflectance. They also developed a nonlinear optimization-based technique that estimates bottom depth and IOPs, using only measured spectral remote sensing reflectance as input. While quite effective, inversion using noisy field data can limit its accuracy. In this research, the nonlinear optimization-based Lee et al. inversion algorithm was used as a baseline method, and it provided the framework for a proposed hybrid evolutionary/classical optimization approach to hyperspectral data processing. All aspects of the proposed implementation were held constant with that of Lee et al., except that a hybrid evolutionary/classical optimizer (HECO) was substituted for the nonlinear method. HECO required more computer-processing time. In addition, HECO is nondeterministic, and the termination strategy is heuristic. However, the HECO method makes no assumptions regarding the mathematical form of the problem functions. Also, whereas smooth nonlinear optimization is only guaranteed to find a locally optimal solution, HECO has a higher probability of finding a more globally optimal result. While the HECO-acquired results are not provably optimal, we have empirically found that for certain variables, HECO does provide estimates comparable to nonlinear optimization (e.g., bottom albedo at 550 nm).
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