From a different perspective, four papers focus on the endogenous sparsity property of the remote sensing data, neatly investigated to improve the application performance. In “Identifying relevant hyperspectral bands using boruta: a temporal analysis of water hyacinth biocontrol” by N. Agjee et al., two band selection methods are combined with random forest classifier to detect the efficacy of a water hyacinth biocontrol agent, driven by a sparsity assumption of the relevant hyperspectral reflectance. Successfully keeping all relevant bands, it is believed that the boruta algorithm can be utilized to undertake the multitemporal monitoring of variable infestation levels on water hyacinth plants. In “Seizing on sparsity in nonlinear hyperspectral unmixing for enhanced image compression” by A. Marinoni and P. Gamba, a strong detection of sparse distribution within the coefficients is embedded to enhance the performance of remotely sensed data compression. In “Endmember initialization method for hyperspectral data unmixing” by R. Wang et al., not only the true endmembers from the spectral library but also those close to the real hyperspectral images (HSI) are extracted, under the prior referred to as finiteness of the responding endmember to a mixed hyperspectral pixel and sparsity of the fractional abundance matrix. In “Super-resolution reconstruction of hyperspectral images using empirical mode decomposition and compressed sensing” by Z. Zhou, the spectral-spatial sparsity of HSI has been elaborately clarified to design a sparse representation reconstruction framework of HSI, which contains empirical mode decomposition, compressed sensing, and principal component analysis.