SISLU-Net: Spatial Information-Assisted Spectral Information Learning Unmixing Network for Hyperspectral Images

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...

Land Cover Subpixel Change Detection using Hyperspectral Images Based on Spectral Unmixing and Post-processing

  The earth is continually being influenced by some actions such as flood, tornado and human artificial activities. This process causes the changes in land cover type. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Today’s remote sensing plays key role in geology and environmental monitoring by its high resolution, wide covering and low cost...

Spectral Unmixing for the Classification of Hyperspectral Images

Spectral mixing is inherent in any finite-resolution digital imagery of a heterogeneous surface, so that mixed pixels are inevitably created when multispectral images are scanned. Solving the spectral mixture problem is, therefore, involved in image classification, referring to the techniques of spectral unmixing. The invention of imaging spectrometers especially promotes the potential of apply...

GPU Implementation of Spatial-Spectral Preprocessing for Hyperspectral Unmixing

Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of ...

Incorporating spatial information in spectral unmixing: A review