The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information. However, the limitation of imaging sensors reduced spatial resolution that causes detail loss. One solution is fuse low (LR-HSI) and panchromatic (PAN) with inverse features get high-resolution (HR-HSI). Most existing fusion methods just focus on small ratios like 4 or 6, which migh...

Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral bands was used...

This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library. Existing semisupervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most such methods assume a linear mixing model. Howeve...

We describe the development of a real-time processing tool for hyperspectral imagery based on off-the-shelf equipment and higher level programming language implementation (C++ and Java). The algorithms we developed are derived from previously introduced spectra matching and feature extraction tools. The first group is based on spectra identification and spectral screening, a method that allows ...

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 ...

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 re...

Species classification is an important task when it comes to the study of specific areas without disrupting the ecosystem. In this paper we chose Indian Pines as the test site to classify it into 16 classes of tree species and compare them with ground truth to obtain the accuracy, using various algorithms with machine learning process. The algorithms used are LDA and SVM. Spectral features of t...

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can benefit from spatial regularization strategies. While existing spatial regularization methods improve the problem conditioning and promote piecewise smooth solutions, ...

Reference data (“ground truth”) maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. ...