Graph Regularized Coupled Spectral Unmixing for Change Detection

This paper presents a methodology of coupled spectral unmixing for multitemporal hyperspectral data analysis. Coupled spectral unmixing simultaneously extracts the sets of spectral signatures of endmembers and respective abundance maps from multiple spectral images with differences in observation conditions and sensor characteristics. The problem is formulated in the framework of coupled nonnegative matrix factorization. A graph regularization that reflects spectral correlation between two images on abundance fractions is introduced into the optimization of coupled spectral unmixing to consider temporal changes of the earth’s surface. An alternating optimization algorithm is investigated using the method of Lagrange multipliers to guarantee a stable convergence. The proposed method was applied to dual-temporal Hyperion images taken over the Fukushima Daiichi nuclear power plant. Experimental results showed that the proposed method can extract essential information on the earth’s surface in a data-driven manner beyond multitemporal data modality

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Detection and identification of effluent gases using invariant hyperspectral algorithms

The ability to detect and identify effluent gases is a problem that has been pursued with limited success. An algorithm to do this would not only aid in the regulation of pollutants but also in treaty enforcement. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate invariant methods of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is also studied at the subpixel level. A library of target gas spectra is generated using a simple radiance model. This results in a more robust representation of the gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithm. An evaluation was carried out to determine the subset of basis vectors that best span the subspace. Two basis vector selection methods are used to determine the subset of basis vectors; Singular Value Decomposition (SVD) and the Maximum Distance Method (MaxD). The Generalized Likelihood Ratio Test (GLRT) was used to determine whether the pixel is more like the target or the background. The target can be either a single species or a combination of gases, however, this study only looks for one gas at a time. Synthetically generated hyperspectral scenes in the longwave infrared (LWIR) region of the electromagnetic spectrum are used for this research. The test scenarios used in this study represented strong and weak plumes with single or multiple gas releases. In this work, strong and weak plumes refer to the release, which is on the order of tens of grams per second and tenths of grams per second, respectively. This work demonstrates the effectiveness of these invariant algorithms for the gas detection and identification problem

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R2> 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission’s Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblage