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.     

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.     

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.     

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.     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.     

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.     

Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level.

The main aim of this paper is to demonstrate that a coarse-resolution land-cover map of the Russian Federation, the ‘TerraNorte’ map at 230 m × 230 m resolution for the year 2010, can be used in combination with a sample of reference forest maps at 30 m resolution to correctly assess forest cover in the Russian federation.

First, an accuracy assessment of the TerraNorte map is carried out through the use of reference forest maps derived from finer-resolution satellite imagery (Landsat Thematic Mapper (TM) sensor). A sample of 32 sites was selected for the detailed identification of forest cover from Landsat TM imagery. A methodological approach is developed to process and analyse the Landsat imagery based on unsupervised classification and cluster-based visual labelling. The resulting forest maps over the 32 sites are then used to evaluate the accuracy of the forest classes of the TerraNorte land-cover map. A regression analysis shows that the TerraNorte map produces satisfactory results for areas south of 65° N, whereas several forest classes in more northern areas have lower accuracy. This might be explained by the strong reflectance of background (i.e. non-tree) cover.

A forest area estimate is then derived by calibration of the TerraNorte Russian map using a sample of Landsat-derived reference maps (using a regression estimator approach). This estimate compares very well with the FAO FRA exercise for 2010 (1% difference for total forested area). We conclude that the TerraNorte map combined with finer-resolution reference maps can be used as a reliable spatial information layer for forest resources assessment over the Russian Federation at national scale.     

Combining per-pixel and object-based classifications for mapping land cover over large areas

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.     

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.     

两种基于空间相关性的遥感亚像元分类制图方法对比分析

为了对比基于Hopfield Neural Network(HNN)和几何绘图的遥感亚像元分类制图方法的具体性能,验证空间相关性原理用于遥感亚像元定位的可行性。以一个研究区的TM影像为对象,通过利用HNN和几何亚像元制图方法,获得该区的亚像元定位结果,对比分析了各方法的视觉效果、精度和时间复杂度。结果表明:空间相关性特性在两种方法中得到了良好的体现,为后续亚像元制图研究应用提供参考。     

Post-processing of interpolation-based super-resolution mapping with morphological filtering and fraction refilling

Interpolation-based super-resolution mapping (SRM) is a popular model to produce a super-resolution land-cover map from coarse-resolution fraction images. This model can maintain the holistic land-cover features; however, it also results in a super-resolution land-cover map that includes many speckle and linear artefacts, due to errors caused by both the interpolation and the label assignment steps. In this article, we propose a novel two-step post-processing algorithm for interpolation-based SRM. The first step is morphological filtering, which is used to eliminate artefacts and to preserve land-cover features in the super-resolution land-cover map produced by interpolation-based SRM. The second step is fraction refilling, which is applied to make the fraction constraints satisfied and the super-resolution land-cover map locally smooth. Based on the application to three simulated images with various interpolation algorithms and morphological filter operations, the performance of the proposed post-processing algorithm was assessed. The results show that the proposed post-processing algorithm increases the accuracy of the super-resolution land-cover map and is suitable for different interpolation-based SRM models.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

Sub-pixel vs. super-pixel-based greenspace mapping along the urban–rural gradient using high spatial resolution Gaofen-2 satellite imagery: a case study of Haidian District,Beijing, China

Greenspace in urban areas is closely related to urban ecosystems, economy, culture, and society. Recently, rapid urban development and expansion are always dominated by a series of human–environment interactions, which can lead to various spatial patterns of urban greenspace especially along the urban–rural gradient. Urban–rural greenspace mapping is therefore of great importance to provide a comprehensive insight for urban planners and managers. In our study, we adopted both the sub-pixel and super-pixel strategies to map the greenspace in Haidian District, Beijing, China. Specifically, the fully constrained linear spectral unmixing and object-based classification methods were implemented as the representatives of sub-pixel and super-pixel strategies, respectively. The high spatial resolution Gaofen-2 multispectral imagery collected in September, 2015 was used in this study. The results showed that the overall accuracies of greenspace mapping by the super-pixel method were higher than those by the sub-pixel method in the selected dense urban, sub-urban, and rural subsets. Obviously, the super-pixel method was more advantageous for mapping greenspace from the high spatial resolution imagery, especially for patches of greenspace in rural and mountain areas. When further comparing these two methods using the medium spatial resolution Landsat-8 imagery, we concluded that the sub-pixel method failed to keep the same levels of greenspace mapping accuracies as those using the high spatial resolution Gaofen-2 imagery but outperformed the super-pixel method especially in the dense urban and sub-urban subsets due to their high degrees of greenspace fragmentation. Furthermore, the sub-pixel method also demonstrated its merits in terms of automation and operability compared to the super-pixel method.     

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。     

Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

The expansion of urban development into wildland areas can have significant consequences, including an increase in the risk of structural damage from wildfire. Land-use and land-cover maps can assist decision-makers in targeting and prioritizing risk mitigation activities, and remote sensing techniques provide effective and efficient methods to create such maps. However, some image processing approaches may be more appropriate than others in distinguishing land-use and land-cover categories, particularly when classifying high spatial resolution imagery for urbanizing environments. Here we explore the accuracy of pixel-based and object-based classification methods used for mapping in the wildland–urban interface (WUI) with free, readily available, high spatial resolution urban imagery, which is available in many places to municipal and local fire management agencies. Results indicate that an object-based classification approach provides a higher accuracy than a pixel-based classification approach when distinguishing between the selected land-use and land-cover categories. For example, an object-based approach resulted in a 41.73% greater accuracy for the built area category, which is of particular importance to WUI wildfire mitigation.     

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.     

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.     

Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models

Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery. However, for waterline mapping problems, the traditional spatial dependence principle of subpixel mapping is not suitable as the water flow is always controlled by the topography. This letter presents a novel algorithm based on a high spatial resolution digital elevation model (DEM) to address the subpixel waterline mapping problem. The waterline was mapped at the subpixel scale with a proposed rule according to the physical features of the water flow and additional information provided by the DEM. The method was evaluated with degraded real remotely sensed imagery at different spatial resolutions. The results show that the proposed method can provide more accurate classifications than the traditional subpixel mapping method. Moreover, the fine spatial resolution DEM can be used as feasible supplementary data for subpixel waterline mapping from coarser spatial resolution imagery.     

Hierarchical Land Cover Classification based on MODIS NDVI Time-series

In this paper,we mainly used MODIS NDVI time-series dataset at 16-days temporal resolution and 250-meters spatial resolution to analyze land cover mapping of northeastern China.We used two different filter methods to fit NDVI time-series dataset,and compared their average classes’ separability based on Jeffries-Matusita distance index.In addition,we made use of hierarchical classification method to complete classification,combined with short-wave infrared spectral reflectance data and DEM.We conformed to the principle that separate area hierarchically into several parts first and then classify each part further,and use a single characteristic band first and then multiple feature bands.In the process of classification,we adopted threshold value method,support vector machine,artificial net neural and C5.0 decision tree classification to distinguish each land-cover type hierarchically.Finally,we evaluated the accuracy of the final classification of study area using known land-cover classification data and high-resolution remote sensing imagery,overall accuracy is 84.61%,Kappa coefficient is 0.8262.