Sub-pixel mapping with point constraints

Abstract. Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Landscape pattern is a mixture of natural and human-managed patches that vary in size, shape, arrangement and is the result of complex interactions of physical, biological, and social forces. At present, landscape pattern indices (LPIs) have become increasingly popular for characterizing landscape pattern and they are commonly calculated from land cover maps extracted from remotely sensed imagery. Pixel-based hard classification methods assign each pixel to one class and always suffer from the mixed pixel problem, and make the resultant land cover maps and extracted LPIs inaccuracy. Moreover, as the pixel size of hard classification maps is identical with the resolution of the remotely sensed imagery, spatial patterns of patches which are smaller than the resolution of remotely sensed imagery are usually ignored, and hard classification maps thus can hardly represent the spatial pattern of finer scale land cover classes due to the grain size effect. In general, soft classification techniques can estimate the class composition of land cover classes, however, their output fraction images provide no indication of how such classes are spatially distributed. Sub-pixel mapping (SPM) is a technology to transform the resulting fraction images of soft classification technologies into a finer scale hard classification map, which can solve the mixed pixel problem and eliminate the influence of grain size effect to a certain extent while providing the spatial representation of land cover targets at the sub-pixel scale. In this research, 33 landscape-level LPIs (for all land cover classes altogether) and 28 class-level LPIs (for individual land cover class) were applied to assess the performance of SPM method in characterizing landscape pattern. Ikonos imagery of Dujiangyan (Sichuan Province, China) representing urban landscape pattern and Landsat TM imagery of suburb of Wuhan (Hubei Province, China) representing rural landscape pattern were used in this study. We derived LPIs from both SPM maps and hard classification maps and found that SPM can better depict landscape pattern than hard classification methods at both the landscape level and class level in most cases.

Various subpixel mapping (SPM) methods have been proposed as downscaling techniques to reduce uncertainty in classifying mixed pixels. Such methods can provide category maps of a higher spatial resolution than the original input images. The aim of this study was to explore and validate the potential of SPM as an alternative method for obtaining land use/land cover (LULC) maps of regions where high-spatial-resolution LULC maps are unavailable. An experimental design was proposed to evaluate the feasibility of SPM for providing the alternative LULC maps. A case study was implemented in the Jingjinji region of China. SPM results for spatial resolutions of 500–100 m were derived from a single 1-km synthetic fraction image using two representative SPM methods. The 1-km synthetic fraction image was assumed to be error free. Accuracy assessment and analysis showed that overall accuracies of the SPM results were reduced from about 85% to 75% with increasing spatial resolution, and that producer’s accuracies varied considerably from about 62% to 93%. SPM performed best when handling areal features in comparison with linear and point features. The highest accuracies were achieved for areas with the lowest complexity. The study concluded that the results from SPM could provide an alternative LULC data source with acceptable accuracy, especially in areas with low complexity and with a large proportion of areal features.

There are a lot of mixed pixels in the remotely sensed imagery, which can seriously limit the utility of classification. Sub-pixel mapping (SPM) is a promising technique to solve this problem. It can generate a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at sub-pixel scale.However, the accuracy and detail are always limited. Especially when the scale factor is large among sub-pixels per pixel, the data volumes are amplified and the sub-pixel distribution becomes complex. The traditional methods are carried out only by the fractions of land cover and the spatial dependence theory, which cannot satisfy the requirement of the SPM. For avoiding the above flaw, a new SPM method based on maximum a posteriori (MAP) model with subpixel/pixel spatial attraction theory aimed at the largescale factor is proposed. First, MAP is proposed to improve the resolution of the fractional images and obtain the initial sub-pixel locations; after that, the pixel swapping algorithm is used to optimize and produce the final SPM result. In this paper, the proposed model is tested by a simple simulated font image and real remotely sensed imagery, which can both demonstrate that it can outperform traditional algorithm with a more accurate sub-pixel scale land cover map.

Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can be applied to improve this situation through deriving the abundances and generating the fractional change map, but it cannot provide the spatial distribution of the subpixels. Subpixel mapping (SPM) is a potential solution to resolve this problem, and is designed to use the proportions to obtain a sharpened thematic map at a subpixel scale. Based on this thought, the subpixel scale land cover change mapping result can be realized by integrating these two key techniques. However, in practice, there is a serious limitation to the detail and accuracy of the result, because when the scale factor between the different resolution images is large, the subpixel configuration is complex and the data volumes will be amplified. Moreover, with the high proportion of the changed area in the whole image, the change detection process at the subpixel level gets more difficult. The SPM technique is generally performed based only on the abundances of each and the spatial dependence assumption, so it cannot satisfy the demand. In order to overcome this shortcoming, several new reasonable subpixel scale change detection rules are defined in this paper, which dictate the land cover change map must be constructed according to the existing high-resolution image. The output from SC and prior information of the subpixel feature arrangement is applied into a modified cellular automata (CA) model, which can be regarded as a more reasonable tool to monitor the subpixel changes to resolve the big-data problem. Two experiments are performed and the results prove that the proposed method can effectively improve the accuracy of the change detection maps of the spatial distribution in a subpixel scale.

Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery

Spatial component is the key and most likely the first element of map making so that accurate spatial information improves the locational perception of map users. In this regard, soft classified satellite imagery conveys class proportions within pixels; however spatial distribution of the sub-pixels remains unknown. So, different visualization techniques (e.g. pie-chart representation of the proportions) are suggested to communicate the detailed land cover information. However, in each of which, the perception of actual spatial location of sub-pixels is definitely difficult for map users. Recently, the Super Resolution Mapping (SRM) techniques have been developed for optimization of the sub-pixels spatial arrangement based on the concepts of spatial dependency. These are relatively new methods which a comprehensive study on their performance and also their decisive parameters is a central issue for sub-pixel land cover mapping. In this research, the binary Pixel Swapping (PS) algorithm, as a prominent SRM algorithm, is developed for multivariate land cover mapping and the accuracy of the proposed method is evaluated in two procedures of independent and dependent of the soft classification error. Likewise, the impact of some parameters (e.g. zoom factor, neighborhood level and weighting function) is investigated on the efficiency of the algorithm. According to the results, the overall accuracy of the PS technique is extremely dependent on the accuracy of its input data (outputs of the soft classification). Furthermore, as a key result of this chapter, it is indicated that by increasing the zoom factor, the overall accuracy of the algorithm decreases. Also, the second level of neighborhood and inverse/square inverse distance functions has demonstrated the highest accuracies. Considering lower values than 5 for zoom factor, overall accuracy of the algorithm is determined higher than 90 % in procedure of optimizing the sub-pixels spatial arrangement.KeywordsLocational perceptionLand coverSub-pixel visualizationSuper resolution mappingPixel swappingAccuracy assessment

In the past decades, sub-pixel mapping algorithms have been extensively developed due to the large number of different applications. However, most of the sub-pixel mapping algorithms are based on single-temporal images, and the results are usually compromised without auxiliary information due to the ill-posed problem of sub-pixel mapping. In this paper, a novel spatial-temporal sub-pixel mapping algorithm based on swarm intelligence theory is proposed for multitemporal remote sensing imagery. Swarm intelligence theory involves clonal selection sub-pixel mapping (CSSM), which evolves the solution by emulating the biological advantage of the human immune system, and differential evolution sub-pixel mapping (DESM), which optimizes the solution by intelligent operations and heuristic searching in the solution pool. In addition, considering the under-determined problem of sub-pixel mapping, the spatial-temporal sub-pixel mapping method is used to obtain the distribution information at a fine spatial resolution from the bitemporal image pair, which exactly regularizes the ill-posed problem. Furthermore, the short-interval temporal information and the fine spatial distribution information within the bitemporal image pair can be integrated for further use, such as timely and detailed land-cover change detection (LCCD). To verify the validation of the swarm intelligence theory based spatial-temporal sub-pixel mapping algorithm, the proposed algorithm was compared with several traditional sub-pixel mapping algorithms, in both synthetic and real image experiments. The experimental results confirm that the proposed algorithm outperforms the traditional approaches, achieving a better sub-pixel mapping result both qualitatively and quantitatively, as well as improving the subsequent LCCD performance.

Accurate land cover mapping by using coarse resolution imageries has been an attractive research topic. Sub-pixel mapping has been proven efficient for allocating sub-pixels within a mixed pixel. The most likely distribution can be determined on the condition of maximized spatial dependence. However, linear land cover like roads and rivers cannot be predicted efficiently because of weaker spatial dependence between and within mixed pixels. To obtain more accurate classification at the sub-pixel scale, an improved sub-pixel mapping method by combining spatial dependence with directivity and connectivity of linear land cover was proposed. Central line of linear land cover was extracted from fraction images to provide site-specific information. Discriminated allocation targets were accordingly designed: both connectivity and directivity were considered as important auxiliary information for allocating linear land cover, whereas only maximized spatial dependence is required for other classes. Then, simulated annealing arithmetic (SAA) was applied to optimize sub-pixel allocation. The method was evaluated visually and quantitatively with the accuracy indices. Compared with the model that considers only spatial dependence, SPM HIIPD method, attraction model and hard classifier (MLC), the improved method can increase classification accuracy at the sub-pixel scale with both simulated imageries and partial SPOT remotely sensed imagery.

In this letter, a novel subpixel mapping (SPM) based on extended random walker (ERW) (SPMERW) is proposed. First, the resolution of the original coarse remote sensing image is upsampled by bicubic interpolation. Second, the class proportions of subpixel are produced by unmixing the upsampled image. Irregular objects are generated by adaptive segmentation of the first principal component of the upsampled image. Third, the class proportions of the object are derived by averaged fusion of the class proportions of subpixel belonging to each object in the segmentation image. Object spatial dependence including the spatial information among and within the objects is obtained by the ERW algorithm. Finally, a class allocation method based on units of the object is utilized to obtain the SPM result according to the object spatial dependence. Experimental results on two remote sensing data sets show that the proposed SPMERW outperforms the state-of-the-art SPM methods.

In this paper, a sub-pixel mapping (SPM) method based on super-resolution then spectral unmixing (SRTSUSPM) is proposed. In the proposed framework, firstly projection onto convex set (POCS) model with the endmembers of interest is applied to original imagery to obtain a high-resolution imagery; then the fraction images are derived from the high-resolution imagery by linear spectral mixture analysis (LSMA); finally hard attribute values on a per sub-pixel basis is implemented to achieve SPM. Experiments show that the higher mapping accuracy can be derived from the proposed SPM method.

In this paper we tackle the problem of data analysis on high dimensional space of hyperspectral images. The remote sensing data analysis is a complex task due to many factors such as the large spectral and spatial variability. In fact, several approaches suffer from the pixel-mixed problem since mixed pixels are often sources of uncertainty and inaccuracy. Although spectral un-mixing techniques can provide abundance fractions for each class in mixed pixels, spatial distribution of these classes remains still unknown. Sub-pixel mapping techniques provide any solutions to the above-mentioned problem. Nevertheless, most of the proposed methods treat one particular type of objects because they assume that mixed pixels in a single image are identical. Sub-pixel mapping methods employing zonal objects are based on the spatial dependence assumption; the same assumption is used for linear objects while considering objects direction. Whereas, in case of encapsulated objects no spatial correlation is applied. As a result, each type of object requires special treatment, hence, the need for object recognition at sub-pixel scales. In this paper to improve sub-pixel object recognition accuracy in coarse spatial resolution image, we develop in this paper a spectro-spatial method based on a graph model, which discriminates between each type of sub-pixel object. The latter is classified into three categories: zonal, linear and small encapsulated objects based on their spatial information in neighboring pixels and their spectral information. In order to determine the suitability and the effectiveness of the proposed approach, experiments are performed with a simulated and a real hyperspectral image.

In this paper, a sub-pixel mapping algorithm based on differential evolution is proposed, namely adaptive differential evolution sub-pixel mapping algorithm (ADESM). In ADESM, the sub-pixel mapping problem becomes one of assigning land cover classes to the sub-pixels while maximizing the spatial dependence index (SDI). In the proposed ADESM algorithm, individuals are represented as a discrete sub-pixel mapping solution by discrete encoding strategy. These individuals are improved by enhanced evolution operators, e.g., mutation, crossover, repair, exchange, insertion, and an effective local search to generate new candidate solutions. Furthermore, ADESM may adaptively obtain the optimal sub-pixel mapping result without user-defined parameters by adaptive parameter-setting strategy. The proposed method was tested using the synthetic real imagery. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of remote sensing imagery.

The soft-then-hard subpixel mapping (STHSPM) algorithm is a type of subpixel mapping (SPM) algorithm consisting of soft class value (between 0 and 1) estimation and hard class allocation for each subpixel. This letter presents a new class allocation method for STHSPM algorithm. As an extension of our previous work in which subpixels for classes are decided in units of classes (UOC), the new approach, named adaptive UOC (AUOC), improves UOC with adaptive visiting order of classes. In AUOC, the visiting order of classes within each coarse pixel is determined based on the local structure rather than the global structure in UOC. Experiments on three remote sensing images show that AUOC is able to improve UOC in terms of SPM accuracy, particularly for SPM with small zoom factors.

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.