Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays increasingly significant role to solve this problem. In article, we present comprehensive survey the NMF-based methods proposed for unmixing. Taking NMF model as baseline, show how improve by uti...

Hyperspectral images are 3D data sets collected over an x, y grid, where the pixel at each x, y is composed of a spectrum. In hyperspectral unmixing, such a data set X composed of n observed spectra with p wavelengths or spectral bands is decomposed to identify the pure component spectra. Such data sets are found in airborne land imaging studies, biomedical and art history investigations as wel...

Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model always tackle this problem because of its highly ill-posed nature. In article, we introduce linear (LMM)-based end-to-end deep neural network named SNMF-Net for hyperspectral unmixing. shares alternating architecture benefits...

Traditional breast cancer imaging methods using microwave Nearfield Radar Imaging (NRI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal, in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. In this paper, we describe a novel unmixing algorithm for detecting...

"In this paper the authors address the problem of interpreting and classifying aggregate data sources and draw parallels between tasks commonly encountered in image processing and census analysis. Both of these fields already have a range of standard classification tools which are applied in such situations, but these are hindered by the aggregate nature of the input data. An approach to ¿unmix...

Remote sensing images contain a lot of mixed image pixels, but it is difficult to classify these pixels. If the number of pixel’s end-member is regarded as unchangeable, the traditional pixel unmixing algorithm cannot get a good result. In this paper we develop a new method of selective end-members for pixel unmixing based on the fuzzy ARTMAP neural network, which firstly compares the pixel’s s...

: Aiming at the disadvantage of hard per-parcel classification which can't solve the difficulty of mixed parcel resulting in the low accuracy, a new method of soft per-parcel classification is presented, that is linear mixed parcel unmixing. Based on the linear spectral theory for the parcel unmixing, the predicted fraction value is assigned to a parcel. The RMSE results show that the accuracy ...

Independent Components Analysis nds a linear transformation to variables which are maximally statistically independent. We examine ICA from the point of view of maximising the likelihood of the data. We elucidate how scaling of the unmixing matrix permits a \static" nonlinearity to adapt to various marginal densities and we demonstrate a new algorithm that uses generalised exponentials function...

■ Spatial pixel sizes for multispectral and hyperspectral sensors are often large enough that numerous disparate substances can contribute to the spectrum measured from a single pixel. Consequently, the desire to extract from a spectrum the constituent materials in the mixture, as well as the proportions in which they appear, is important to numerous tactical scenarios in which subpixel detail ...

The operational use of MERIS images can be hampered by the presence of clouds. This work presents a cloud screening algorithm that takes advantage of the high spectral and radiometric resolutions of MERIS and the specific location of some of its bands to increase the cloud detection accuracy. Moreover, the proposed algorithm provides a per-pixel probabilistic map of cloud abundance rather than ...