DLR HySU—A Benchmark Dataset for Spectral Unmixing

Spectral Unmixing for Information Extraction

“From pixels to processes” – this the shortest and most essential formulation of all studies, experiments and investigations carried out in the field of Earth remote sensing observations. This formulation reveals two main directions of data interpretation: the first related to classification and feature retrieval, and the second associated with multi-temporal aspects of remotely sensed data and...

Associative morphological memories for spectral unmixing

Arabian Horse Identification Benchmark Dataset

The lack of a standard muzzle print database is a challenge for conducting researches in Arabian horse identification systems. Therefore, collecting a muzzle print images database is a crucial decision. The dataset presented in this paper is an option for the studies that need a dataset for testing and comparing the algorithms under development for Arabian horse identification. Our collected da...

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such ...

Unmixing and target recognition in hyper - spectral

4 We present two new linear algorithms that perform unmixing in hyper-spectral 5 images and then recognize their targets whose spectral signatures are given. The 6 first algorithm is based on the ordered topology of spectral signatures. The second 7 algorithm is based on a linear decomposition in each pixel’s neighborhood. The sought 8 after target can occupy subor above pixel. These algorithms...