Abundance estimation is an important step of quantitative analysis of hyperspectral remote sensing data. Due to physical interpretation, sum-to-one and non-negativity constraints are generally imposed on the abundances of materials. This paper presents a geometric approach to fully constrained linear spectral unmixing using variable endmember sets for the pixels. First, an improved method for s...

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been d...

Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and `2,1 norm (SFL) that can deal with ...

The goal of this paper is to design compact support basis spline functions that best approximate a given filter (e.g., an ideal Lowpass filter). The optimum function is found by minimizing the least square problem (l2 norm of the difference between the desired and the approximated filters) by means of the calculus of variation; more precisely, the introduced splines give optimal filtering prope...

In line with the pressures of energy shortage and economic development, Chinese government has adopted a series of measures and policies to promote the exploitation and utilization efficiency of electric power. China is urgently reconsidering its electric power development level and coordinating between power supply and demand sides. Therefore, in this paper, Chinese industrial structure of ele...

An efficient image resizing algorithm in the compressed domain with mixed field/frame-mode macroblocks is proposed. A 16 16 field/frame-mode macroblock is converted into an 8 8 reduced block in the discrete cosine transform (DCT) domain using a modified inverse DCT (IDCT) kernel which performs stronger lowpass filtering than the simple truncation in the DCT domain. Experimental results show tha...

We introduce a fast and high performance image subsampling method using feedforward artificial neural networks (FANNs). Our method employs a pattern matching technique to extract local edge information within the image, in order to select the FANN desired output values during the supervised training stage. Subjective and objective evaluations of experimental results using still images and video...

Projection pursuit is the search for interesting low-dimensional projections of high-dimensional data. It optimizes projection indices, which increase with the interestingness of the projection image. Most classical approaches equate interestingness with non-gaussianity. However, in cluster analysis one should more be interested in departure from unimodality. The dip is an efficient nonparametr...

Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the p...