A Reappraisal of the Period Spectral Analysis

The purpose of this paper is to revive interest in the periodogram approach to time series analysis which, at present, is only of historical interest and is seldom used. During the late 1940's, when it was realized that the smoothed periodogram could be used to estimate the spectral density of a stationary time series, the method was impractical because of the amount of computations. This is no longer the case, but not realized by many applied workers. In this paper the smoothed periodogram is considered from the point of view of its spectral window. The results are compared with two standard spectral windows by a method which avoids defining a bandwidth. The spectral windows are normalized so that they have the same variance, and plotted. The user can then choose the window which best suits his needs. Rejection filtering, trigonometric regression and cross-spectra analysis are discussed. An example is given in which the spectra and cross-spectrum of a bivariate time series are estimated.     

What a histogram can really tell the classifier

This paper presents a simple and efficient clustering technique based on the partitioning of the data histogram. The clustering technique was developed in the context of a study of possible unsupervized classification procedures for multispectral earth imagery. The ultimate goal was on-board data compression, the algorithmic production of thematic maps.

The incoming raw spectral data is first reduced to its two principal (Karhunen-Loêve) components, the histogram of which is then partitioned into natural classes on the sole weight of evidence of the global statistics of the imagery. During the course of the study, it became clear that some connection existed between the proposed philosophy and professor Thom's novel theory of “catastrophes”.⁽¹⁾ A simple metric is added to the histogram topology. The metric uses both Shannon's and Fisher's notions of self information. In the domain of definition of the histogram, zones corresponding to the natural classes become separated by a no man's land, an inter-class zone. Under the same formulation, the metric is “Euclidean” on the class zone, “non-Euclidean”, i.e. “Lorentz” on the inter-class zone. This methodology and the underlying philosophy were tested in practice, and encouraging results were obtained.     

Robust blind motion deblurring using near-infrared flash image

In light-limited situations, camera motion blur is one of the prime causes for poor image quality. Recovering the blur kernel and latent image from the blurred observation is an inherently ill-posed problem. In this paper, we introduce a hand-held multispectral camera to capture a pair of blurred image and Near-InfraRed (NIR) flash image simultaneously and analyze the correlation between the pair of images. To utilize the high-frequency details of the scene captured by the NIR-flash image, we exploit the NIR gradient constraint as a new type of image regularization, and integrate it into a Maximum-A-Posteriori (MAP) problem to iteratively perform the kernel estimation and image restoration. We demonstrate our method on the synthetic and real images with both spatially invariant and spatially varying blur. The experiments strongly support the effectiveness of our method to provide both accurate kernel estimation and superior latent image with more details and fewer ringing artifacts.     

Research on ALI Image Fusion Algorithm based on Improved Brovey Transform

Aiming at the problems of spectral information loss and spectral distortion in traditional Brovey Transform (BT) fusion, the adaptive weighted average is introduced to improve it. Taking EO-1 ALI multispectral imagery as an example, a new multispectral image fusion algorithm based on improved BT is proposed. Information entropy, average gradient, correlation coefficient and root mean square error are used to comprehensively evaluate and compare the fusion effects of this algorithm, so as to verify the effectiveness and superiority of the multispectral image fusion algorithm based on improved BT. Experimental results show the multispectral fusion image using this improved algorithm has better spectral information and spatial resolution, and its visual effects and spatial texture features have been significantly improved, and the color information of the source image has been well extended, and the brightness is relatively moderate; this improved algorithm can reduce the loss and distortion of spectral information in the fusion process, and has obvious advantages in maintaining spectral information and clarity compared with the traditional BT fusion method.     

Cladyn,a new compressor for numerical color TV and multispectral images

The CLADYN compressor banks on the topological properties of a color image to achieve the highest possible data rate reduction with the minimum amount of visible degradation.In the case of numerical color TV, the input data at 166 MB/s is the CCIR imposed luminance Y and chrominance B- Y, RY components. The compressor output is at 25.26 MB/s, exclusive of sound channels and error correcting overhead. High reconstructed picture quality is obtained (at the rate of 6.56/1) without the use of any TV temporal redundancy. In the case of multispectral images, excellent image reconstruction is obtained with a signal to quantization noise ranging from 40 to 50 dBs and with a data rate reduction factor higher than 4/1 for scenes comprising 3–5 spectral channels. This type of compressor is not very sensitive to channel misregistration, is robust to the propagation of the transmission errors and outputs fixed length words.     

Stability and dimensionality of Karhunen-Loêve multispectral image expansions

The Karhunen-Loêve (K.L.) expansion is a useful tool for the representation, pre-processing and orthogonal coding of multispectral imagery: Each spatial pixel is analysed independently as the K.L. transform is taken in the spectral dimension, i.e. along the various N spectral channels. The eigenvectors are those of the covariance matrix. The (principal) eigenimages are thus “false color” images, which can be viewed without decoding as the spatial topology is unchanged, and the higher order principal images present a strong contrast enhancement.⁽¹⁾ These principal images are also uncorrelated, a very desirable feature for many applications including clustering.⁽²⁾

The source dependency of the eigenvectors, however, introduces “instability” in the form of pronounced statistical noise on some principal images. This paper gives the results of a numerical study carried out on a 7 channel Daedalus Multispectral Scene. The uncertainties of the eigenvalues and eigenvectors are evaluated from two “drawings” of the pixels of the raw data.

Both the numerical results of the study and the direct viewing of the principal images show that three out of the seven have so much noise that they do not yield any useful information. Only the first two principal images have excellent stability, and they contain most of the total contrast variance of the scene. Two other principal images of lower order are also stable, but, contribute very little to the total contrast variance. These images carry texture information rather than homogeneous zone clustering information.     

Implementation of real-time constrained linear discriminant analysis to remote sensing image classification

In this paper, we investigate the practical implementation issues of the real-time constrained linear discriminant analysis (CLDA) approach for remotely sensed image classification. Specifically, two issues are to be resolved: (1) what is the best implementation scheme that yields lowest chip design complexity with comparable classification performance, and (2) how to extend CLDA algorithm for multispectral image classification. Two limitations about data dimensionality have to be relaxed. One is in real-time hyperspectral image classification, where the number of linearly independent pixels received for classification must be larger than the data dimensionality (i.e., the number of spectral bands) in order to generate a non-singular sample correlation matrix R for the classifier, and relaxing this limitation can help to resolve the aforementioned first issue. The other is in multispectral image classification, where the number of classes to be classified cannot be greater than the data dimensionality, and relaxing this limitation can help to resolve the aforementioned second issue. The former can be solved by introducing a pseudo inverse initiate of sample correlation matrix for R^-1 adaptation, and the latter is taken care of by expanding the data dimensionality via the operation of band multiplication. Experiments on classification performance using these modifications are conducted to demonstrate their feasibility. All these investigations lead to a detailed ASIC chip design scheme for the real-time CLDA algorithm suitable to both hyperspectral and multispectral images. The proposed techniques to resolving these two dimensionality limitations are instructive to the real-time implementation of several popular detection and classification approaches in remote sensing image exploitation.     

基于小波特征和模拟退火的遥感图象快速聚类算法

不同遥感对象，光谱曲线的突变点位置差异很大，不同尺度的小波变换可有效提取这些突变特征，在此基础上，用小波特征相关系数描述像素的近似程度，取代一般聚类算法以欧氏距离为基础的聚类概念，聚类结果可准确反映遥感对象内容，基于小波特征抽取和模拟退火的多光谱遥感图象快速聚类算法，通过扩展频段，增加特征点的个数以丰富类的特性，对空间数据进行均匀抽样产生聚类空间，采用模拟退火技术和逐步降低聚类规模的方法，快速实现全局最优的聚类中心，类内评价最优代表作为聚类中心，保证类特性的持续性和强壮性，而且解决了K－means聚类的参数选择问题，最后采用TM多光谱遥感图象进行参数分析和算法比较，验证了该算法分类快速准确，且参数控制灵活，因此基于小波特征抽取和模拟退火的多光谱遥感图象快速聚类算法有较好的应用前景。     

Pan-sharpening via multi-scale and multiple deep neural networks

Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.     

基于Sentinel-2数据的天山山地针叶林识别方法研究

阴影是影响山地针叶林遥感识别精度的关键因素。选取天山一块面积约为10 000 km²的区域为案例,基于太阳高度角和方位角差异较大的两期Sentinel-2影像,从遥感数据阴影分布的时相特性、分类特征以及分类器选择三方面进行综合分析,提出了一种适用于天山山地针叶林的遥感综合分类方案。该综合分类方案首先开展阴影识别以及阴影再分类以排除阴影对针叶林识别的影响;然后筛选出了海拔、归一化差值植被指数（NDVI）、红光到近红外波段斜率、蓝光波段、红光波段、短波红外波段和坡度作为区分天山山地针叶林的重要特征;最后比较支持向量机（Support Vector Machine,SVM）、随机森林（Random Forest,RF）和BP神经网络（Back Propagation Neural Network,BPNN）3种分类器的分类效果。结果表明：采用地形校正方法来消除山体阴影的效果不但不明显,反而还会造成过矫正现象,从而影响后续的针叶林识别,但利用太阳高度角和方位角差异较大的两期影像开展阴影识别以及阴影再分类来排除阴影对针叶林识别的影响,可使针叶林的总体精度提高1.3%~3.7%;SVM、RF和BPNN 3种分类器都能取得较好的山地针叶林识别精度,但SVM分类器的分类精度最高,其总体分类精度和Kappa系数分别是93.33%和0.87。该遥感综合分类方案经参数调整之后有望应用于北方干旱半干旱区的其他山地针叶林区域。