Xianchuan Yu

Multi-scale hybrid saliency analysis for region of interest detection in very high resolution remote sensing images

Abstract Researchers have recently been performing region of interest detection in such applications as object recognition, object segmentation, and adaptive coding. In this paper, a novel region of interest detection model that is based on visually salient regions is introduced by utilizing the frequency and space domain features in very high resolution remote sensing images. First, the frequency domain features that are based on a multi-scale spectrum residual algorithm are extracted to yield intensity features. Next, we extract the color and orientation features by generating space dynamic pyramids. Then, spectral features are obtained by analyzing spectral information content. In addition, a multi-scale feature fusion method is proposed to generate a saliency map. Finally, the detected visual saliency regions are described using adaptive threshold segmentation. Compared with existing models, our model eliminates the background information effectively and highlights the salient detected results with well-defined boundaries and shapes. Moreover, an experimental evaluation indicates promising results from our model with respect to the accuracy of detection results.

A fast mixing matrix estimation method in the wavelet domain

In the field of blind image separation (BIS) based on the sparse component analysis, separation efficiency and accuracy are directly affected by the number of clustering samples. To address this problem, a new algorithm for the detection of points in the Haar wavelet domain was proposed in which only single source contributions occur. The algorithm identified the single source points (SSPs) by comparing the absolute direction between the diagonal and horizontal components of the Haar wavelet coefficients of mixed images. After screening the SSPs, the wavelet coefficients of the images are sparser. The experimental results showed that, compared to the conventional method, the proposed algorithm could estimate the mixing matrix faster and more accurately, and it allowed identification of latent variables via statistical histograms.

Research of independent component analysis

Independent component analysis (ICA) is a statistical technique to decompose multivariate data into statistically independent components. It could be applied to mine data of medical, economy or telecommunication systems, and to analyze data of GIS systems for agriculture or environment applications. To solve the problem of blind source separation, this paper introduces the theory and developments of ICA. The analyses of different methods as well as the comparisons with each other on objective functions and optimization algorithms are given. Finally, some problems of ICA to be solved are discussed

Performance evaluation of different references based image fusion quality metrics for quality assessment of remote sensing Image fusion

This paper focus on eight frequently used Image fusion quality metrics (IFQMs), which are correlation coefficient, relative bias, structure similarity index, root-mean-square error, cross entropy, mutual information, spectral angle mapper and ERGAS to check their ability to measure the quality similarity among the images based on three different reference images. We have evaluated the performance of the IFQMs by considering in the three aspects: (1) Consistency with the other similar IFQMs; (2) Robustness to different testing images; (3) Consistency with the visual evaluations. Experimental results show that taking resampled multispectral image as reference, ERGAS outperforms other IFQMs. Taking original low resolution multi-spectral image as reference, correlation coefficient and SAM have the best performance. Taking the high resolution pan or SAR image as reference, root-mean-square error and ERGAS perform well. Relative bias is not suitable for fusion image evaluation due to its poor performance in all the three aspects.

A remote sensing image fusion method based on feedback sparse component analysis

Abstract We propose a new remote sensing image (RSI) fusion technique based on sparse blind source separation theory. Our method employs feedback sparse component analysis (FSCA), which can extract the original image in a step-by-step manner and is robust against noise. For RSIs from the China–Brazil Earth Resources Satellite, FSCA can separate useful surface feature information from redundant information and noise. The FSCA algorithm is therefore used to develop two RSI fusion schemes: one focuses on fusing high-resolution and multi-spectral images, while the other fuses synthetic aperture radar bands. The experimental results show that the proposed method can preserve spectral and spatial details of the source images. For certain evaluation indexes, our method performs better than classical fusion methods.

Embedded hybrid coding for lossy to lossless image compression using integer wavelet transform

The IWT allows both lossy and lossless compression using a single bitstream. In this paper, a low-complexity, efficient embedded hybrid-coding algorithm so-called embedded subband partitioning block arithmetic coding (ESPBA) is presented. The new algorithm firstly selects a segmentation threshold based on the integer powers of two. All image coefficients above this threshold are encoded using simple quadtree partitioning scheme. The residual image coefficients below the threshold are encoded using block arithmetic coding based on context modeling. Experimental results show that ESPBA method has better PSNR performance than based-IWT SPIHT for lossy compression. The lossless compression performance of the presented method is comparable to JPEG-LS and SPIHT

Application of Fast Independent Component Analysis on Extracting the Information of Remote Sensing Imagery

The preprocessing of remote sensing imagery (RSI) has great importance on the results of the classification. In this paper, the algorithm of fast independent component analysis (ICA) and its application to the remote sensing imagery classification are presented, and different parameter has different effect on information extraction with ICA. The remote sensing imagery for experiment is from different areas, in different time by several sensors. In succession, a maximum likelihood estimation (MLE) supervised classification method is used to classify the original images and feature images after ICA. As a result, with distinct characters of original images, choosing varied bands and parameters can get better independent component images. The classification result based on feature image is more credible than on image pixel. As for fast independent component analysis, it can remove the correlation of remote sensing imagery, gain high order statistical independent features however some texture information is lost and have better decorrelation result than PCA, which will make for classification

New Region of Interest Image Coding Based on Multiple Bitplanes Up-Down Shift Using Improved SPECK Algorithm

Image compression based on region of interest (ROI) means to code the important regions in an image with high quality and to code other regions (Background-BG) with relatively low quality. Based on this idea, a flexible ROI image coding method called multiple bitplanes up-down shift (MBUShift) is presented. First, the new method divides all original bitplanes into three parts according to different importance of ROI and BG bitplanes. Second, the most significant ROI and BG bitplanes are shifted up, the least significant ROI and BG bitplanes are shifted down, and another bitplanes are not shifted. Third, the improved SPECK algorithm based on quadtree partitioning is applied for encoder and decoder. The unique characteristics of the new method lie in the combination of good ROI compression performance and low algorithm complexity. Experimental results indicate that the proposed method significantly outperforms the previous ROI coding schemes in overall ROI coding performance

The theory of disjunctive kriging and its application in grade estimate

Linear estimation methods such as ordinary and simple kriging commonly fail to provide unbiased estimates of recovered ore tonnage and metal content which means that a mining project can be exposed to undue risk. Nonlinear estimation, such as the Gaussian disjunctive kriging (DK) technique provide a mean of calculating unbiased estimates of ore and metal content over any cut-off range and mining unit size combination. The disadvantage of this method is the requirement of an assumption of strict stationarity. It supposes that we have known all bivariate distributions of regional variables (Zalpha, Z beta) and (Z0, Zbeta) in which the values of Zalpha, Zbeta is known and the value of Z0 is to be estimated. The paper contents of Gaussian anamorphosis, varigram and structure analysis and Hermite polynomials. The application of these estimation methods to a deposit is described. The study carries out DK and ordinary kriging (OK) for a suit of 3D drill data of a multi-metal deposit, containing 63 drills and 2 kinds of metal, from which we can see DK is more perfect and easier. A discussion of the results from a practical point of view is also given

Classification of landsat TM image based on non negative matrix factorization

Non-negative matrix factorization (NMF) is one of the recently emerged dimensionality reduction methods. Unlike other methods, NMF is based on non-negative constraints, which allows learn parts from objects. In this paper a performance comparison of PCA and NMF, which are data preprocessing algorithms in remote sensing imagery classification, is presented. PCA and NMF are applied to a remote sensing imagery (128 times 128), obtained from Shunyi, Beijing. For classification, a maximum likelihood classification method is used for the preprocessed data. The results show that classification with NMF has more confident results than that with PCA. NMF keeps more abundant texture information.