The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in tematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the metho...

In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyperspectral unmixing has been a challenging task in hyperspectral data exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fracti...

The invasions of non-native species of vegetation pose significant threats to natural environments at all geographical scales. Saltcedar has been commonly treated as one of the several most threatening invasive species in U.S. in the next ten years. The spatial extent and density of infestation by saltcedar in the Rio Grande floodplain has been poorly understood in the past. Remote sensing prov...

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 ...

Geo-registration is the task of assigning geospatial coordinates to the pixels of an image and placing them in a geographic coordinate system. However, the process of geo-registration can impair the quality of the image. This paper studies this topic by applying a comparison methodology to uncorrected and geo-registered airborne hyperspectral images obtained from the RIT SHARE 2012 data set. Th...

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. Proceedings of IEEE International, Geoscience and Remote Sensing Symposium. IGARSS 04, 5:3257 – 3260, Sept. 2004. [2] M. Estlick M. Leeser, J. Theiler and J. J. Szymanski. Design tradeoffs in a hardware implementation of the K-Means clustering algorithm. Proceedings of the IEEE. Sensor Array and Multichannel Signal Processing Workshop., pages 520 – 524, March 2000. [3] Sam...

Endmember extraction and spectral unmixing is a very challenging task in multispectral/hyperspectral image processing due to the incompleteness of information. In this paper, a new method for endmember extraction and spectral unmixing of hyperspectral images is proposed, which is called as minimum distance constrained nonnegative matrix factorization (MDC-NMF). After being compared with a newly...

Abstract—Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no info...