Super-resolution mapping using Hopfield Neural Network with panchromatic imagery

Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.     

结合超分辨率重建的神经网络亚像元定位方法

遥感影像中普遍存在着混合像元,如何分析和解译混合像元一直是人们研究的热点。亚像元定位方法是将混合像元分解成为亚像元,并赋予不同的端元组分,以提高影像整体分类精度的一种技术。本文在神经网络亚像元定位模型的基础上,结合超分辨率重建理论,提出一种新型的BPMAP模型,在每一个类别的组成分图像与亚像元定位图像之间建立起高、低分辨率的观测模型,采用最大后验估计（MAP）算法对BP神经网络的定位结果进行约束,最终确定混合像元内部各组分合适的空间位置。通过对模拟的简单图像和长江三峡地区的ETM影像进行实验,结果表明,与神经网络模型相比,本文方法能够更加有效地解决亚像元定位的问题,进一步消除定位过程中产生的误差,提高精度。     

Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

This article presents a vectorial boundary-based sub-pixel mapping (VBSPM) method to obtain the land-cover distribution with finer spatial resolution in mixed pixels. With inheritance from the geometric SPM (GSPM), VBSPM first geometrically partitions a mixed pixel using polygons, and then utilizes a vectorial boundary extraction model (VBEM), rather than the rasterization method in GSPM, to determine the location and length of each edge in the polygon, while these edges are located at the boundary of and within the interior of the mixed pixel. Furthermore, VBSPM uses a decay function to manage the mixed pixels along the image boundary region due to the missing parts of their neighbours. Finally, a ray-crossing algorithm is employed to determine the land-cover class of each sub-pixel in terms of vectorial boundaries. The experiments with artificial and remotely sensed images have demonstrated that VBSPM can reduce the inconsistency between the boundaries of different land-cover classes, approximately calculating errors with an odd zoom factor, and achieve more accurate sub-pixel mapping results than the hard classification methods and GSPM.     

Sub-pixel mapping of remotely sensed imagery with hybrid intra- and inter-pixel dependence

Sub-pixel mapping of remotely sensed imagery is often performed by assuming that land cover is spatially dependent both within and between image pixels. Intra- and inter-pixel dependencies are two widely used approaches to represent different land-cover spatial dependencies at present. However, merely using intra- or inter-pixel dependence alone often fails to fully describe land-cover spatial dependence, making current sub-pixel mapping models defective. A more reasonable object for sub-pixel mapping is maximizing both intra- and inter-pixel dependencies simultaneously instead of using only one of them. In this article, the differences between intra- and inter-pixel dependencies are discussed theoretically, and a novel sub-pixel mapping model aiming to maximize hybrid intra- and inter-pixel dependence is proposed. In the proposed model, spatial dependence is formulated as a weighted sum of intra-pixel dependence and inter-pixel dependence to satisfy both intra- and inter-pixel dependencies. By application to artificial and synthetic images, the proposed model was evaluated both visually and quantitatively by comparing with three representative sub-pixel mapping algorithms: the pixel swapping algorithm, the sub-pixel/pixel attraction algorithm, and the pixel swapping initialized with sub-pixel/pixel attraction algorithm. The results showed increased accuracy of the proposed algorithm when compared with these traditional sub-pixel mapping algorithms.     

基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究

混合像元普遍存在于遥感图像数据中。与传统的硬分类(Hard Classification)方法相比,在处理混合像元时,软分类(Soft Classification)技术可以避免信息丢失;但是,通过软分类技术获得的结果,仍然无法确定各分类在像元中的具体位置。子像元制图(或超分辨率制图、亚像元制图)技术能将软分类技术得到的结果转化为更高分辨率的图像,它能兼得软分类和硬分类两者的优势。将遗传算法的一个变种-单亲遗传算法应用于子像元制图,结合子像元/像元空间吸引模型,单亲遗传算法能直接获得子像元制图结果。以合成的图像和实际的土地覆盖图像为实验对象,通过目视比较和定量精度评价,与硬分类的结果相比,该方法能取得更高的制图精度和更好的结果。     

Sub-pixel Mapping with Partheno-Genetic Algorithm and Sub-pixel/pixel Spatial Attraction Model

Mixed pixels are widely presented in remotely sensed images.Soft classification techniques can avoid the loss of information comparing to hard classification methods while handling mixed pixels.However,the assignment to these classes by soft classification does not specify the location in the pixel.Sub-pixel mapping (or super-resolution mapping) is a technique which designed to use the information obtained by soft classification to get a sharpened image and it can incorporate benefits of both hard and soft classification techniques.In this paper,a variation of genetic algorithm,named as partheno-genetic algorithm (PGA),is developed to accomplish the sub\|pixel mapping.With the sub-pixel/pixel attraction model,PGA can achieve sub-pixel mapping in a straightforward one-pass process.It is evaluated with artificial and degraded land cover images by visual and quantitative classification accuracy indices.The results show this method can increase accuracy while compared to hard classification.     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

基于各向异性Markov随机场的遥感影像亚像元尺度建筑物提取

基于遥感影像的建筑物自动提取方法容易受混合像元影响,目标提取精度不高。亚像元定位可以提取亚像元尺度地物分布信息,减轻混合像元对目标提取结果造成的影响。传统亚像元定位模型采用各向同性邻域描述地物的空间相关性,并没有考虑地物特有的形状信息,难以满足建筑物提取的需要。在考虑建筑物光谱特征的基础上,建立了平行与垂直于目标建筑物主方向的各向异性邻域,并采用基于各向异性Markov随机场的亚像元定位模型进行了亚像元尺度的建筑物提取。基于QuickBird多光谱数据与AVIRIS高光谱数据的实验结果表明,该模型提取的建筑物不仅具有更高的空间分辨率,而且能够较好地保持建筑物边缘与角点的形状信息,是一种有效的亚像元尺度建筑物提取方法。     

Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.     

Spectral–spatial based sub-pixel mapping of remotely sensed imagery with multi-scale spatial dependence

Sub-pixel mapping (SPM) is a technique used to obtain a land-cover map with a finer spatial resolution than input remotely sensed imagery. Spectral–spatial based SPM can directly apply original remote-sensing images as input to produce fine-resolution land-cover maps. However, the existing spectral–spatial based SPM algorithms only use the maximal spatial dependence principle (calculated at the sub-pixel scale) as the spatial term to describe the local spatial distribution of different land-cover features, which always results in an over-smoothed and discontinuous land-cover map. The spatial dependence can also be calculated at the coarse-pixel scale to maintain the holistic land-cover pattern information of the resultant fine-resolution land-cover map. In this article, a novel spectral–spatial based SPM algorithm with multi-scale spatial dependence is proposed to overcome the limitation in the existing spectral–spatial based SPM algorithms. The objective function of the proposed SPM algorithm is composed of three parts, namely spectral term, sub-pixel scale based spatial term, and coarse-pixel scale based spatial term. Synthetic multi-spectral, degraded Landsat multi-spectral and real IKONOS multi-spectral images are employed in the experiments to validate the performance of the proposed SPM algorithm. The proposed algorithm is evaluated visually and quantitatively by comparing with the hard-classification method and two traditional SRM algorithms including pixel-swapping (PS) and Markov-random-field (MRF) based SPM. The results indicate that the proposed algorithm can generate fine-resolution land-cover maps with higher accuracies and more detailed spatial information than other algorithms.     

Using genetic algorithms in sub-pixel mapping

In remotely sensed images, mixed pixels will always be present. Soft classification defines the membership degree of these pixels for the different land cover classes. Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. Genetic algorithms combined with the assumption of spatial dependence assign a location to every sub-pixel. The algorithm was tested on synthetic and degraded real imagery. Obtained accuracy measures were higher compared with conventional hard classifications.     

遥感影像亚像元定位研究综述

遥感影像亚像元定位是在混合像元分解基础上,利用地物空间分布特征确定不同地物类型在混合像元中的具体位置,得到亚像元尺度的地物分类图,是一种有效解决混合像元空间不确定性的方法。首先介绍遥感影像亚像元定位的基本概念,分析亚像元定位的理论模型和求解算法;然后总结亚像元定位模型的误差来源、精度评价方法以及结果不确定性的表达手段,同时讨论利用辅助数据源提高亚像元定位精度的主要方法;最后对亚像元定位的研究趋势做了进一步展望。     

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

Super-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.     

Improving super-resolution mapping through combining multiple super-resolution land-cover maps

Super-resolution mapping (SRM) is an ill-posed problem, and different SRM algorithms may generate non-identical fine-spatial resolution land-cover maps (sub-pixel maps) from the same input coarse-spatial resolution image. The output sub-pixels maps may each have differing strengths and weaknesses. A multiple SRM (M-SRM) method that combines the sub-pixel maps obtained from a set of SRM analyses, obtained from a single or multiple set of algorithms, is proposed in this study. Plurality voting, which selects the class with the most votes, is used to label each sub-pixel. In this study, three popular SRM algorithms, namely, the pixel-swapping algorithm (PSA), the Hopfield neural network (HNN) algorithm, and the Markov random field (MRF)-based algorithm, were used. The proposed M-SRM algorithm was validated using two data sets: a simulated multispectral image and an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral image. Results show that the highest overall accuracies were obtained by M-SRM in all experiments. For example, in the AVIRIS image experiment, the highest overall accuracies of PSA, HNN, and MRF were 88.89, 93.81, and 82.70%, respectively, and these increased to 95.06, 95.37, and 85.56%, respectively for M-SRM obtained from the multiple PSA, HNN, and MRF analyses.     

基于局部连续性与全局相似性的光谱保持型亚像元映射

遥感图像的像元级分类精度受混合像元的影响. 亚像元映射以像元分解获得的丰度值为基础,在地物分布规律的约束下,细化估计各类地物的亚像元级分布模式. 本文同时考虑了地物分布的空间与光谱信息,提出了一种基于局部连续性与全局相似性的光谱保持型亚像元映射算法. 针对地物的空间分布特性,提出了利用类内离散度对局部连续性进行建模,并通过相似分布像元表示误差引入全局相似性约束项. 针对地物的光谱特性,采用最小化光谱误差约束了亚像元映射过程中的光谱无失真性. 模拟数据与真实数据上的实验结果表明,本文算法比其他同类算法具有更高的估计精度,且更适合于实际应用.     

Super-resolution land cover mapping with indicator geostatistics

Many satellite images have a coarser spatial resolution than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Super-resolution or sub-pixel mapping aims at providing a fine resolution map of class labels, one that displays realistic spatial structure (without artifact discontinuities) and reproduces the coarse resolution fractions. In this paper, existing approaches for super-resolution mapping are placed within an inverse problem framework, and a geostatistical method is proposed for generating alternative synthetic land cover maps at the fine (target) spatial resolution; these super-resolution realizations are consistent with all the information available.More precisely, indicator coKriging is used to approximate the probability that a pixel at the fine spatial resolution belongs to a particular class, given the coarse resolution fractions and (if available) a sparse set of class labels at some informed fine pixels. Such Kriging-derived probabilities are used in sequential indicator simulation to generate synthetic maps of class labels at the fine resolution pixels. This non-iterative and fast simulation procedure yields alternative super-resolution land cover maps that reproduce: (i) the observed coarse fractions, (ii) the fine resolution class labels that might be available, and (iii) the prior structural information encapsulated in a set of indicator variogram models at the fine resolution. A case study is provided to illustrate the proposed methodology using Landsat TM data from SE China.     

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.     

基于元胞自动机模型的遥感图像亚像元定位

由于遥感图像中普遍存在混合像元,因此传统分类方法得到的结果通常会存在较大误差,应用混合像元分解技术,虽然可以得到混合像元中各端元组分的丰度,但是却不能得到各端元组分的空间分布状态,而亚像元定位则是在混合像元分解的基础上,将混合像元剖分为亚像元,再利用端元组分的丰度及像元空间分布的特点,将亚像元赋予不同端元组分来得到各端元组分的空间分布情况,以提高遥感图像分类的精度。为了更好地解决亚像元定位问题,结合亚像元定位的理论模型,提出了一种新的元胞自动机模型,并通过模拟数据和实际数据对该模型进行了检验,结果表明,该模型是一种简单有效的解决亚像元定位问题的方法。     

Selecting the spatial resolution of airborne MSS imagery for small-scale agricultural mapping

The spatial resolution determines the number of data and amount of information in a remotely sensed image of a given scene. The 'optimal' spatial resolution may be defined as that which maximizes the information per pixel, and this maximum is realized when the semivariance at a lag of one pixel (the average squared difference between neighbouring pixels) is maximized. For mapping, a spatial resolution should be chosen that is much finer than the 'optimal' spatial resolution as defined above. Airborne MSS images in both red and near-infrared wavelengths for three different dates and two sites were investigated to determine a spatial resolution suitable for mapping spatial variation in agricultural fields in the U.K. The spatial resolution most appropriate for mapping the spatial variation in the images was between 0.5 m and 3 m.     

Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of oversmoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.