Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

Non-linear mixture modelling without end-members using an artificial neural network

Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.     

Symmetric deformable image registration via optimization of information theoretic measures

The use of information theoretic measures (ITMs) has been steadily growing in image processing, bioinformatics, and pattern classification. Although the ITMs have been extensively used in rigid and affine registration of multi-modal images, their computation and accuracy are critical issues in deformable image registration. Three important aspects of using ITMs in multi-modal deformable image registration are considered in this paper: computation, inverse consistency, and accuracy; a symmetric formulation of the deformable image registration problem through the computation of derivatives and resampling on both source and target images, and sufficient criteria for inverse consistency are presented for the purpose of achieving more accurate registration. The techniques of estimating ITMs are examined and analytical derivatives are derived for carrying out the optimization in a computationally efficient manner. ITMs based on Shannon’s and Renyi’s definitions are considered and compared. The obtained evaluation results via registration functions, and controlled deformable registration of multi-modal digital brain phantom and in vivo magnetic resonance brain images show the improved accuracy and efficiency of the developed formulation. The results also indicate that despite the recent favorable studies towards the use of ITMs based on Renyi’s definitions, these measures are seen not to provide improvements in this type of deformable registration as compared to ITMs based on Shannon’s definitions.     

Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches

Fully-fuzzy classification approaches have attracted increasing interest recently. These approaches allow for multiple and partial class memberships at the level of individual pixels and accommodate fuzziness in all three stages of a supervised classification of remotely sensed imagery. A fully-fuzzy classification strategy may be deemed more objective and correct than partially-fuzzy approaches where fuzziness is only accommodated in one or two of the three classification stages. This paper describes two approaches to the fully-fuzzy classification of remotely sensed imagery: a statistical approach based on a modified fuzzy c-means clustering algorithm performed in a supervised mode and an artificial neural network based approach. This is followed by the documentation of a case study using Landsat Thematic Mapper (TM) data of an Edinburgh suburb. Both approaches were applied to derive fully-fuzzy classifications of land cover, with fuzzy ground data, critical for training and testing the classifications, derived from indicator kriging. Results confirmed the superiority of fully-fuzzy over their respective partially-fuzzy classification counterparts, which is beneficial given their more relaxed requirements for training pixels (i.e. training pixels need not be pure). Similar accuracies were obtained with the artificial neural network and statistical approaches to classification. It is suggested that due emphasis must be placed on derivation and analysis of fuzzy ground data as well as fuzzy classified data in order to further improve fully-fuzzy classifications.     

基于知识和遥感图像的神经网络水质反演模型

为进一步提高遥感图像水质反演的精度，提出了一种基于知识和遥感图像相结合的神经网络水质反演模型。该模型利用遥感图像数据以及与水质相关的知识数据作为BP神经网络的输入，经训练后，确定神经网络的结构，在训练好的BP神经网络基础之上对水质进行反演。以中国太湖为例进行实证研究，实验中，使用的知识数据包括太湖的地理信息知识和先对太湖TM图像上的水域解译进而对水质进行分类的知识。实验结果表明，本文提出的水质反演模型较常规的线性回归模型和传统的神经网络模型有更高的反演精度。     

An IR and visible image sequence automatic registration method based on optical flow

IR–visible camera registration is required for multi-sensor fusion and cooperative processing. Image sequences can provide motion information, which is useful for sequence registration. The existing methods mainly focus on registration using moving objects which are observed by both cameras. However, accurate motion feature extraction for a whole moving object is difficult, because of the complex environment and different imaging mechanism of two sensors. To overcome this problem, we use motion features associated with single pixels in the two image sequences to carry out automatic registration. A normalized optical flow time sequence for each image pixel is constructed. The matching of pixels between the IR image and the visible light image is carried out using a fast similarity measurement and a three stage correspondence selection method. Finally cascaded random sample consensus is adopted to remove outlying matches, and least-square method and Levenberg–Marquardt method are used to estimate the transformation from the IR image to the visible image. The effectiveness of our method is demonstrated using several real datasets and simulated datasets.     

Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network

Mixed pixels are a major problem in mapping land cover from remotely sensed imagery. Unfortunately, such imagery may be dominated by mixed pixels, and the conventional hard image classification techniques used in mapping applications are unable to appropriately represent the land cover of mixed pixels. Fuzzy classification techniques can, however, accommodate the partial and multiple class membership of mixed pixels, and be used to derive an appropriate land cover representation. This is, however, only a partial solution to the mixed pixel problem in supervised image classification. It must be reognised that the land cover on the ground is fuzzy, at the scale of the pixel, and so it is inappropriate to use procedures designed for hard data in the training and testing stages of the classification. Here an approach for land cover classification in which fuzziness is accommodated in all three stages of a supervised classification is presented. Attention focuses on the classification of airborne thematic mapper data with an artificial neural network. Mixed pixels could be accommodated in training the artificial neural network, since the desired output for each training pixel can be specified. A fuzzy land cover representation was derived by outputting the activation level of the network's output units. The activation level of each output unit was significantly correlated with the proportion of the area represented by a pixel which was covered with the class associated with the unit (r>0.88, significant at the 99% level of confidence). Finally, the distance between the fuzzy land cover classification derived from the artificial neural network and the fuzzy ground data was used to illustrate the accuracy of the land cover representation derived. The dangers of hardening the classification output and ground data sets to enable a conventional assessment of classification accuracy are also illustrated; the hardened data sets were over three times more distant from each other than the fuzzy data sets.     

卫星遥感数据的地表直射光辐射计算与改正

定量讨论了像元地表的起伏和地理位置的不同对地表直射光辐射的影响以及这种影响造成的像元遥感数值的变化。并在此基础上建立了像元地表直射光辐射的计算公式和像元遥感数据直射光分量的改正公式。     

Multiscale comparison of remote-sensing data for linear woody vegetation mapping

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.     

Nonlinear estimation of subpixel proportion via kernel least square regression

Spectral mixture analysis is an efficient approach to spectral decomposition of hyperspectral remotely sensed imagery, using land cover proportions which can be estimated from pixel values through model inversion. In this paper, a kernel least square regression algorithm has been developed for nonlinear approximation of subpixel proportions. This procedure includes two steps. The first step is to select the feature vectors by defining a global criterion to characterize the image data structure in the feature space and the second step is the projection of pixels onto the feature vectors and the application of classical linear regressive algorithm. Experiments using simulated data, synthetic data and Enhanced Thematic Mapper (ETM)+ data have been carried out, and the results demonstrate that the proposed method can improve proportion estimation. By using the simulated and synthetic data, over 85% of the total pixels in the image are found to lie between the 10% difference lines, and the root mean square error (RMSE) is less than 0.09. Using the real data, the proposed method can also perform satisfactorily with an average RMSE of about 0.12. This algorithm was also compared with other widely used kernel based algorithms, i.e. support vector regression and radial basis function neutral network and the results show that the proposed algorithm outperforms other algorithms about 5% in subpixel proportion estimation.     

An entropy-based image registration method using image intensity difference on overlapped region

Image registration is a fundamental procedure in image processing that aligns two or more images of the same scene taken from different times, different viewpoints, or even different sensors. It is reasonable to orientate two images by matching corresponding pixels or regions that are considered identical. Based on this concept, this paper proposes a novel image registration method that applies the information theorem on intensity difference data. An entropy-based objective function is then developed according to the histogram of the intensity difference. The intensity difference represents the absolute gray-level difference of the corresponding pixels between the reference and sensed images over the overlapped region. The proposed registration method is to align the sensed image onto the reference image by minimizing the entropy of the intensity difference through iteratively updating the parameters of the similarity transformation. For performance evaluation, the proposed method is compared with the two exiting registration methods in terms of eight test image sets. The experiment is divided into two scenarios. One is to investigate the sensitivity (i.e., robustness) of the objective functions in these three different methods; the other is to verify the effectiveness of the proposed method. Through the experimental results, the proposed method is shown to be very effective in image registration and outperforms the other two methods over the test image sets.     

Performance characteristics of the indicator classifier on simulated image data

Classification of remotely sensed imagery into groups of pixels having similar spectral reflectance characteristics is conducted classically by comparing the spectral signature of unknown pixels with those of training pixels of known ground cover type. Thus classification methods use only the spectral characteristics of image data without considering the spatial aspects or the relative location of an unknown pixel with respect to pixels from the training data set. An indicator classifier was introduced in 1992 that combines spatial and spectral information in a decision model. In this Letter the performance of this classifier is tested on simulated image data with known mineral targets and varying spatial variability and noise. It is demonstrated that incorporating spatial continuity into the classification process may largely increase the accuracy of the resulting classified images.     

IHS和小波变换结合多源遥感影像融合质量对小波分解层数的响应

随着遥感技术的快速发展以及遥感数据的广泛应用,影像的融合处理已成为多源遥感影像信息聚合、获取高质量空间影像的有效途径。基于SPOT全色和多光谱、TM多光谱遥感数据,运用IHS和小波变换相结合的融合方法,进行了不同来源影像融合、融合图像质量对小波分解层数的响应以及这种响应对研究区域面积的敏感性分析。结果表明,多源影像之间的IHS和小波变换相结合的融合方法明显地改善了影像的质量;融合图像质量与原始影像空间分辨率相关,如经1层小波变换融合,TM,SPOT融合图像熵值的增幅分别为2095%,019%。小波融合图像质量对小波分解的层数的敏感性较强,在小波分解层数为2,3或4时,都能获得高质量的融合图像;小波分解层数等于或大于5时融合图像质量下降,7是大幅下降的临界层数。融合图像质量对小波分解层数的响应特性对面积大小变化是敏感的,特别是小面积图像,为此,实际应用中需特别注意最佳分解层数问题。     

Land cover post-classifications by Markov chain geostatistical cosimulation based on pre-classifications by different conventional classifiers

The recently proposed Bayesian Markov chain random field (MCRF) cosimulation approach, as a new non-linear geostatistical cosimulation method, for land cover classification improvement (i.e. post-classification) may significantly increase classification accuracy by taking advantage of expert-interpreted data and pre-classified image data. The objective of this study is to explore the performance of the MCRF post-classification method based on pre-classification results from different conventional classifiers on a complex landscape. Five conventional classifiers, including maximum likelihood (ML), neural network (NN), Support Vector Machine (SVM), minimum distance (MD), and k-means (KM), were used to conduct land cover pre-classifications of a remotely sensed image with a 90,000 ha area and complex landscape. A sample dataset (0.32% of total pixels) was first interpreted based on expert knowledge from the image and other related data sources, and then MCRF cosimulations were performed conditionally on the expert-interpreted sample dataset and the five pre-classified image datasets, respectively. Finally, MCRF post-classification maps were compared with corresponding pre-classification maps. Results showed that the MCRF method achieved obvious accuracy improvements (ranging from 4.6% to 16.8%) in post-classifications compared to the pre-classification results from different pre-classifiers. This study indicates that the MCRF post-classification method is capable of improving land cover classification accuracy over different conventional classifiers by making use of multiple data sources (expert-interpreted data and pre-classified data) and spatial correlation information, even if the study area is relatively large and has a complex landscape.     

A hierarchical methodology framework for multisource data fusion in vegetation classification

This Letter presents a new methodological framework for a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery. The uniqueness of the approach is that the overall structure of the fusion system is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors. The framework consists of two key components: an automated multisource image registration system and a hierarchical model for multi-sensor and multi-temporal data fusion.     

Super‐resolution mapping of the waterline from remotely sensed data

Methods for mapping the waterline at a subpixel scale from a soft image classification of remotely sensed data are evaluated. Unlike approaches based on hard classification, these methods allow the waterline to run through rather than between image pixels and so have the potential to derive accurate and realistic representations of the waterline from imagery with relatively large pixels. The most accurate predictions of waterline location were made from a geostatistical approach applied to the output of a soft classification (RMSE = 2.25 m) which satisfied the standards for mapping at 1 : 5000 scale from imagery with a 20 m spatial resolution.     

纸币清分系统中的纸币污损检测

针对纸币上常见的笔划及撕裂, 提出了一种基于均匀性特征的污损检测方法. 首先利用均匀性特征判定待检纸币上可能存在污损的区域, 然后进行图像配准, 确定这些区域在参考图像上的对应位置, 并逐像素进行比较, 最终判定待检图像的污损状况.     

Evaluation and accuracy assessment of high‐resolution IFSAR DEMs in low‐relief areas

The use of digital elevation models from remotely sensing systems has been restricted in the past to high‐relief areas due to the lack of appropriate resolution and accuracy to map micro‐relief variability in low relief areas. Interferometric synthetic aperture radar, a new technology that provides detailed elevation models from remotely sensed data, is evaluated. Main characteristics of this data are highlighted. Accuracy assessment is tested in detail for two high‐resolution acquisition modes using higher accuracy sources of data. The accuracy results using the root mean square (rms) error were better than expected according to mission specifications. However, at the checkpoint locations where the signal backscatter generates an elevation measure, the accuracy depends considerably upon the site‐specific surface characteristics, such as the land use, above ground biomass, adjacent forest areas and infrastructure features located within surrounding pixels.     

基于MPI的遥感影像高效能并行处理方法研究

采用基于不同尺度下的面向特征基元的影像分析方法对高分辨率遥感影像进行基于MPI的处理,即在对常规的影像数据划分方法进行总结分析的基础上,提出了基于特定环境下的非均匀数据划分策略;在进行基于影像数据库的MPI并行处理时,提出了一种新的数据流分配方法。处理结果表明,这两种方法均能够在一定环境下取得比常规方法更高的效率。     

Factors affecting remotely sensed snow water equivalent uncertainty

State-of-the-art passive microwave remote sensing-based snow water equivalent (SWE) algorithms correct for factors believed to most significantly affect retrieved SWE bias and uncertainty. For example, a recently developed semi-empirical SWE retrieval algorithm accounts for systematic and random error caused by forest cover and snow morphology (crystal size — a function of location and time of year). However, we have found that climate and land surface complexities lead to significant systematic and random error uncertainties in remotely sensed SWE retrievals that are not included in current SWE estimation algorithms. Joint analysis of independent meteorological records, ground SWE measurements, remotely sensed SWE estimates, and land surface characteristics have provided a unique look at the error structure of these recently developed satellite SWE products. We considered satellite-derived SWE errors associated with the snow pack mass itself, the distance to significant open water bodies, liquid water in the snow pack and/or morphology change due to melt and refreeze, forest cover, snow class, and topographic factors such as large scale root mean square roughness and dominant aspect. Analysis of the nine-year Scanning Multichannel Microwave Radiometer (SMMR) SWE data set was undertaken for Canada where many in-situ measurements are available. It was found that for SMMR pixels with 5 or more ground stations available, the remote sensing product was generally unbiased with a seasonal maximum 20 mm average root mean square error for SWE values less than 100 mm. For snow packs above 100 mm, the SWE estimate bias was linearly related to the snow pack mass and the root mean square error increased to around 150 mm. Both the distance to open water and average monthly mean air temperature were found to significantly influence the retrieved SWE product uncertainty. Apart from maritime snow class, which had the greatest snow class affect on root mean square error and bias, all other factors showed little relation to observed uncertainties. Eliminating the drop-in-the-bucket averaged gridded remote sensing SWE data within 200 km of open water bodies, for monthly mean temperatures greater than − 2 °C, and for snow packs greater than 100 mm, has resulted in a remotely sensed SWE product that is useful for practical applications.