5 research outputs found
Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments
This letter presents a novel hyperspectral endmember extraction approach that integrates a tensor-based decomposition scheme with a probabilistic framework in order to take
advantage of both technologies when uncovering the signatures
of pure spectral constituents in the scene. On the one hand,
statistical unmixing models are generally able to provide accurate
endmember estimates by means of rather complex optimization
algorithms. On the other hand, tensor decomposition techniques
are very effective factorization tools which are often constrained
by the lack of physical interpretation within the remote sensing field. In this context, this letter develops a new hybrid
endmember extraction approach based on the decomposition
of the probabilistic tensor moments of the hyperspectral data.
Initially, the input image reflectance values are modeled as a
collection of multinomial distributions provided by a family of
Dirichlet generalized functions. Then, the unmixing process is
effectively conducted by the tensor decomposition of the thirdorder probabilistic tensor moments of the multivariate data.
Our experiments, conducted over four hyperspectral data sets,
reveal that the proposed approach is able to provide efficient and
competitive results when compared to different state-of-the-art
endmember extraction methods
Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis
This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches