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Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

In this paper, a graph-based nonlocal total variation method (NLTV) isproposed for unsupervised classification of hyperspectral images (HSI). Thevariational problem is solved by the primal-dual hybrid gradient (PDHG)algorithm. By squaring the labeling function and using a stable simplexclustering routine, an unsupervised clustering method with randominitialization can be implemented. The effectiveness of this proposed algorithmis illustrated on both synthetic and real-world HSI, and numerical results showthat the proposed algorithm outperforms other standard unsupervised clusteringmethods such as spherical K-means, nonnegative matrix factorization (NMF), andthe graph-based Merriman-Bence-Osher (MBO) scheme.

In this paper, a graph-based nonlocal total variation method (NLTV) isproposed for unsupervised classification of hyperspectral images (HSI). Thevariational problem is solved by the primal-dual hybrid gradient (PDHG)algorithm. By squaring the labeling function and using a stable simplexclustering routine, an unsupervised clustering method with randominitialization can be implemented. The effectiveness of this proposed algorithmis illustrated on both synthetic and real-world HSI, and numerical results showthat the proposed algorithm outperforms other standard unsupervised clusteringmethods such as spherical K-means, nonnegative matrix factorization (NMF), andthe graph-based Merriman-Bence-Osher (MBO) scheme.

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques.

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques.