Hyperspectral remote sensing technique provides fine and detailed spectral information by contiguous and narrow spectral channels. For the traditional classification algorithms, most of them are based on spectral information; spatial information which is useful for the hyperspectral data analysis is paid a little attention to. So, hyperspectral image classification based on effective combinatio...

This article reviews our research activities in the area of optical mammography and relates them to the historical developments and the current state and trends in the field. The guiding threads for this article are the roles played in optical mammography by spatial and spectral information. The first feature, spatial information, is limited by the diffusive nature of light propagation but can ...

Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at subpixel level and estimate their corresponding proportions (fractional abundances). Recently, rapid development deep learning networks has provided us with a new method solve problem spectral unmixing. In this paper, we propose spatial-information-assisted informat...

Multispectral remotely sensed images composed information over a large range of variation on frequencies (information) and these frequencies change over different regions (irregular or frequency variant behavior of the signal) which need to be estimated properly for an improved classification [1, 2, 3]. Multispectral remote sensing (RS) image data are basically complex in nature, which have bot...

Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Th...

The traditional approach of classifying multispectral and hyperspectral imagery begins with clustering in feature space, followed by labeling the classes in the image space. To overcome some of the disadvantages of this sequential approach is to consider spatial constraints during clustering, for example by using Markov Random Fields. We propose in this paper an agent based clustering method as...

Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments h...