This research focuses on the very first step in the analysis of an image, the point at which one assumes no prior knowledge about the statistical characteristics of the pixels in the image and where little or nothing is known about the size and shape of the objects to be detected. Therefore, the only available option is to look for a point (or group of points) that deviates so much from other p...

This paper will address an important question in machine learning: What kind of network architectures work better on what kind of problems? A projection pursuit learning network has a very similar structure to a one hidden layer sigmoidal neural network. A general method based on a continuous version of projection pursuit regression is developed to show that projection pursuit regression works ...

This work proposes methods for hyperspectral image analysis in both situations viz., (i) when concurrent groundtruth is unavailable and (ii) when available. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. Ordinal optimization approach to PP determines a set of “good enough proje...

We test the use of hyperspectral sensors for the early detection of the invasive denseflowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correctio...

In this paper, we present a L1 regularized projection pursuit algorithm for additive model learning. Two new algorithms are developed for regression and classification respectively: sparse projection pursuit regression and sparse Jensen-Shannon Boosting. The introduced L1 regularized projection pursuit encourages sparse solutions, thus our new algorithms are robust to overfitting and present be...

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group inform...

Various spatial filtering techniques have been developed to process digital Landsat data. This research aimed to filter the same digital data within the frequency domain, and involved the use of the Butterworth lowpass and highpass filters. The lowpass filter is used primarily for the reduction of noise whereas the highpass filter is used primarily for edge enhancement. The analysis of these tw...

Onboard compression of hyperspectral imagery is important for reducing the burden on downlink resources. Here we describe a novel adaptive predictive technique for lossless compression of hyperspectral data. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that is competitive with the best results from the literature. Al...

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...