two different methods of bayesian segmentation algorithm were used with different band combinations. sequential maximum a posteriori (smap) is a bayesian image segmentation algorithm which unlike the traditional maximum likelihood (ml) classification attempts to improve accuracy by taking contextual information into account, rather than classifying pixels separately. landsat 7 etm+ data with pa...

A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging spatial information. In this work, we exte...

During the last two decades, the number of spectral bands in optical remote sensing technology kept growing steadily going from multispectral (MS) to hyperspectral (HS) data sets. HS images employ hundreds of contiguous spectral bands to capture and process spectral information over a range of wavelenghts, compared to the tens of discrete spectral bands used in MS images (Chang, 2003). This inc...

Over the past decade, confocal Raman microspectroscopic (CRM) imaging has matured into a useful analytical tool to obtain spatially resolved chemical information on the molecular composition of biological samples and has found its way into histopathology, cytology, and microbiology. A CRM imaging data set is a hyperspectral image in which Raman intensities are represented as a function of three...

The modern remote sensing imaging sensors, like those in the IKONOS and QuickBird satellites, are capable of generating panchromatic images with about one meter spatial resolution. The principal objective of fusion in remote sensing is to obtain images that can combine the spectral characteristic of the lowresolution multispectral images with the spatial information of the high-resolution panch...

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Typically, spectral information is inferred pixel-based, while spatial information related to texture, context and geometry are deduced on a per-object basis. Existing feature extraction methods cannot fully utilize both the spectral and spatial information....

Semantic segmentation is a fundamental task in the field of remote sensing image intelligent interpretation and computer vision. Multispectral images have attracted more researchers’ attention because they can accurately describe different types reflection spectra. However, inaccurate multispectral feature description leads to edge semantic ambiguity misclassification small objects. In this art...