Sub-pixel Mapping Research Articles

Subpixel Mapping of Hyperspectral Images Using a Labeled-Unlabeled Hybrid Endmember Library and Abundance Optimization

Classification at subpixel level for a low resolution hyperspectral image (LR HSI) is considered in this article. Using selected labeled samples as labeled endmembers and unsupervised clustering centers of LR HSI as unlabeled endmembers, a hybrid endmember library is constructed for spectral unmixing of LR HSI. The abundances of unlabeled endmembers are used to optimize the estimated fractional abundances of labeled classes within mixed pixels to improve the estimation accuracy. A more accurate subpixel mapping result is then obtained by applying subpixel spatial attraction model with the optimized fractional abundances. To incorporate spatial contextual information and further improve the subpixel mapping performance, a subpixel level segmentation map is generated by applying unsupervised clustering to the upsampled LR HSI, and integrated with the initial subpixel mapping result by decision fusion. Experimental results demonstrate that the proposed method remarkably outperforms state-of-the-art subpixel mapping methods, including the corresponding ones with or without spatial contextual information incorporation.

Fine Extraction of Water Boundaries Based on an Improved Subpixel Mapping Algorithm

The fine extraction of water boundaries is of great significance for water resource monitoring, water environment monitoring, and flood prevention. MODIS images are widely used for water extraction due to their high temporal resolution, wide coverage, gratuity, and long observation period. However, owing to their low spatial resolution, the water boundary results are often blurred. It is difficult to extract water boundaries accurately. The subpixel mapping algorithm can solve this problem. In this article, Dongting Lake and its surroundings are adopted as the experimental area. The digital elevation model (DEM) is used to modify the subpixel/pixel spatial attraction model (SPSAM) mapping results. The proposed algorithm is referred to as the DEM-modified SPSAM (D-MSPSAM). Based on the visual results of the two sets of experiments, the modified results suitably maintain the spatial details of the water, and many of the underestimations caused by the similarity of the spectral characteristics of the surroundings to those of the water have been corrected. In this paper, the accuracy of Landsat-8 water extraction is used as a reference. Based on the quantitative results, the D-MSPSAM method has a higher extraction accuracy than the traditional threshold method, and the accuracies of the extraction for high water and low water have been increased by 3.56 percentage points and 2.77 percentage points, respectively. Furthermore, these results also confirm the potential application of DEM data for flood submergence extraction and provide new ideas for the improvement of the subpixel mapping model. The proposed method can accurately generate water distribution maps in a practical and economical way.

Spectral–Spatial–Temporal MAP-Based Sub-Pixel Mapping for Land-Cover Change Detection

The maximum a posteriori (MAP) estimation model-based sub-pixel mapping (SPM) method is an alternative way to solve the ill-posed SPM problem. The MAP estimation model has been proven to be an effective SPM approach and has been extensively developed over the past few years, as a result of its effective regularization capability that comes from the spatial regularization model. However, various spatial regularization models do not always truly reflect the detailed spatial distribution in a real situation, and the over-smoothing effect of the spatial regularization model always tends to efface the detailed structural information. In this article, under the scenario of time-series observation by remote sensing imagery, the joint spectral–spatial–temporal MAP-based (SST_MAP) model for SPM is proposed. In SST_MAP, a newly developed temporal regularization model is added to the MAP model, based on the prerequisite for a temporally close fine image covering the same study region. This available fine image can provide the specific spatial structures most closely conforming to the ground truth for a more precise constraint, thereby reducing the over-smoothing effect. Furthermore, the three dimensions are mutually balanced and mutually constrained, to reach an equilibrium point and achieve restoration of both smooth areas for the homogeneous land-cover classes and a detailed structure for the heterogeneous land-cover classes. Four experiments were designed to validate the proposed SST_MAP: three synthetic-image experiments and one real-image experiment. The restoration results confirm the superiority of the proposed SST_MAP model. Notably, under the background of time-series observation, SST_MAP provides an alternative way of land-cover change detection (LCCD), achieving both detailed spatial-scale and high-frequency temporal LCCD observation for the study case of urbanization analysis within the city of Wuhan in China.

A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images

A new subpixel mapping (SPM) method based on a super-resolution convolutional neural network (SRCNN) is proposed to generate subpixel land cover maps for hyperspectral images. The SRCNN is used to restore the image spatial resolution from a coarse input image, which is equivalent to interpolation. First, an efficient subpixel convolutional neural network, which is a state-of-the-art SRCNN, is utilized to calculate the subpixel soft class value via a transfer learning strategy. Then, a classifier is used to transform the subpixel soft class values to hard-classified land cover maps with the constraint of fraction images. Experiments on three different hyperspectral images demonstrate that the SPM accuracy of the proposed SRCNN-based method is significantly better than those of three traditional SPM methods. In addition, the SRCNN-based SPM method has a simplified calculation process, does not require training data, and is less time consuming. This article provides a new solution for SPM of hyperspectral images.

Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?

Abstract Characterizing landscape patterns is an important analytical step towards understanding the effects of physical layouts on ecological and social processes. While a continuous representation of landscape structure has great potential as a realistic alternative to traditional patch-based models, its empirical merits have been limited by the lack of measures for quantifying patterns from such continuous surface. This paper examines the utility of Gray-Level Co-Occurrence Matrix (GLCM) indices as spatial metrics applied to the landscape level for measuring underlying spatial properties. Eight GLCM indices (contrast, dissimilarity, homogeneity, energy, entropy, mean, variance, correlation) are compared to most commonly used 18 landscape metrics (LMs) featuring landscape composition, aggregation, dominance, dispersion, and shape complexity, with an application to urban tree canopy landscape. Two different types of map, sub-pixel tree canopy cover percentage map versus binary tree-pixel map, are used to compute GLCM indices and class-level LMs with a moving window approach across 4556 focal points. The data, extracted from the National Land Cover Database (NLCD) and the National Agricultural Imagery Program (NAIP), characterize the city of Columbus and Franklin County, Ohio. Correlation and regression analyses demonstrate that there is a strong and robust analogy between textural traits implied by GLCM indices and patch-based characteristics measured by LMs. Four LM components generated by principal component analysis contribute differently to individual GLCM indices, enabling more nuanced interpretation of GLCM indices in terms of LMs. The identified meanings consist of a unique mix of patch abundance, aggregation, dispersion, large patch dominance, patch size variability, and landscape homogeneity. The prediction of landscape patterns by GLCM indices increases in accuracy with landscape size, to a scale comparable to census tracts, while staying robust to the variation in GLCM bin width. GLCM indices can serve as reliable indicators of spatial configuration, and therefore provide an effective tool for researchers to better utilize continuous landscape models.

Subpixel Mapping Based on Hopfield Neural Network With More Prior Information

Subpixel mapping based on the Hopfield neural network (HNN) is a technique to handle mixed pixels for obtaining the spatial distribution information of land cover. However, the original low-resolution remote sensing image may contain some uncertainties, such as the diversity of the land cover classes and the limitation of the resolution of the satellite sensor, the existing HNN is unable to fully utilize the prior information of the original image. In order to resolve this problem, an improved HNN (I-HNN) is proposed in this letter. In the proposed I-HNN, additional prior information of the original image is supplied by adding a new processing path to the existing HNN. To validate the effectiveness of the proposed method, two experiments are conducted on real hyperspectral images. The obtained results demonstrate that the proposed I-HNN outperforms the existing HNN. Moreover, the I-HNN does not require any auxiliary data.

Subpixel mapping of urban built-up areas using spatial-spectral information from satellite multispectral remote sensing imagery

ABSTRACTA novel method for subpixel mapping of urban built-up areas (SMUBA) using spatial-spectral information from satellite multispectral remote sensing imagery is proposed. SMUBA contains two terms, a spatial term and a spectral term. First, a spatial attraction model is used to produce the spatial term. The Normalized difference built-up index is then utilized to obtain the spectral term. Finally, a particle swarm optimization algorithm is used to optimize the two terms and obtain the final mapping result. Since the spatial-spectral information of the urban built-up areas from multispectral imagery is fully utilized by the two terms, the final mapping result is improved. Experimental results on two Landsat 8 Operational Land Imager data show that SMUBA produces better mapping results than state-of-the-art subpixel mapping methods. Moreover, the proposed method does not need any prior shape information.

Subpixel Land-Cover Mapping Based on Extended Random Walker

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.

Multiobjective Sparse Subpixel Mapping for Remote Sensing Imagery

Subpixel mapping (SPM) of remote sensing imagery is aimed at generating a classification map with a finer spatial resolution based on the abundance maps. The sparse subpixel mapping (SSM) method reformulates the SPM problem into a spatial pattern linear regression problem based on the preconstructed subpixel patch dictionary. However, in the SSM model, the optimization of the ${L}0$ -norm is a nonconvex NP-hard problem, so the ${L}1$ -norm is used to replace the ${L}0$ -norm to obtain an approximate solution, and the selection of the optimal weight parameter between multiple terms is difficult. Thus, in this paper, a novel multiobjective SSM (MOSSM) framework for remote sensing imagery is proposed, which transforms the SSM problem into a multiobjective optimization problem. In MOSSM, first, the sparsity term is accurately modeled using the ${L}0$ -norm instead of the ${L}1$ -norm to avoid the potential errors caused by the ${L}1$ -norm, and an evolutionary algorithm is used to directly optimize the ${L}0$ -norm. Second, a subfitness-based multiobjective evolutionary algorithm is employed to simultaneously optimize the fidelity term, the sparsity term, and the spatial prior term, and to generate a set of optimal sparse coefficients to balance these three terms. Thus, there is no need to determine sensitive weight parameters. Finally, two spatial prior terms, which can be applied to the overcomplete dictionary, are presented in the proposed MOSSM-TV and MOSSM-L algorithms to incorporate the spatial correlation of subpixels. Experiments were conducted with two synthetic images and two real data sets, and the results were compared with those of ten other SPM algorithms to demonstrate the effectiveness of the proposed method.

Subpixel Land Cover Mapping Based on Dual Processing Paths for Hyperspectral Image

The subpixel mapping (SPM) technique can handle coarse fractional images derived by unmixing coarse original hyperspectral (HS) image to produce a fine land cover map at the subpixel scale. A popular SPM approach is a two-step model. It first increases the spatial resolution of coarse fractional images by subpixel sharpening to produce fine fractional images and then assigns class labels to each subpixel by the class allocation method. However, there is only a single processing path of the current SPM algorithm, and the information type of the fine fractional images is not rich. To enrich the information type, SPM based on dual processing paths (DPP) is proposed. DPP contains two processing paths, namely spatial–spectral path and multiscale path. First, the coarse original HS image and the high spatial resolution multispectral image are fused by component substitution to produce the fine fractional images with more spatial–spectral information in the spatial–spectral path. At the same time, deep Laplacian pyramid networks are used to obtain the fine fractional images with multiscale information in the multiscale path. The fine fractional images from the two paths are then integrated to generate the improved fraction images with multiscale spatial–spectral information. Finally, the multiscale spatial–spectral information is utilized to allocate class labels by the class allocation method. Experimental results on three real HS remote sensing data show that the proposed DPP outperforms the other SPM methods, demonstrating the effectiveness of the use of DPP in enriching the information type of the fine fractional images.

Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models

Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.

Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran

ABSTRACT This study uses remote sensing image-processing to enhance recognition of areas with mineral abundance halo, which can be associated with porphyry mineralisation of the Urumieh-Dokhtar Magmatic Belt (UDMB) in western Yazd. Granitoid intrusions of Oligocene-Miocene age in UDMB hosts the most important Porphyry Copper Deposits (PCDs) of Iran such as Darezereshk and Aliabad PCDs. Diagnostic spectral absorption features of six shortwave infrared (SWIR) and three visible and near-infrared (VNIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were applied to determine the alteration minerals and their abundance. In this research reflectance data from various representative alteration types throughout the study area were measured by the FieldSpec-3 spectroradiometer and applied for mapping minerals. Different remote sensing analytical techniques, including the Principal Component Analysis (PCA), band ratio, relative absorption band depth, and MTMF, were applied to enhance mapping the alteration minerals. Field works including rock sampling, petrography and field spectroradiometer followed up the alteration mapping. Field observation implies that, the vein-type mineralisation above the cupola of this porphyry system is preserved in the western part, although it was eroded in the eastern area. Consequently, PCDs potential is expected to be higher in the western area.

RESEARCH TRENDS IN HYPERSPECTRAL IMAGERY DATA

Remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands forms a major part of hyperspectral imaging technology. Currently, a wide range of data sets are obtained continuously from hyperspectral remote sensing, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial and research purposes. These data sets give a vast opportunity to explore number of areas where research can be carried out. Areas such as subpixel mapping, super resolution, target detection, compression and retrieval find a vast scope for research work.However, among all the research topics retrieval forms an important topic for discussion due to large amount of data sets being processed at very small interval of time. Excessiveness of information revealed from these data sets also complicates access of users to images they are interested in. In this study, an in-depth review of current research challenges in Hyperspectral Image retrieval techniques has been done to specify research gaps and trends in this subject.

Spatiotemporal Subpixel Geographical Evolution Mapping

In recent decades, spatiotemporal subpixel mapping (SSM) approaches have been extensively developed to deal with the mixed-pixel problem by incorporating fine spatial resolution images with the same field of view from different acquisition times. This is an alternative to the conventional subpixel mapping (SPM) method, which is based on only monotemporal images. SSM has become one of the state-of-the-art SPM approaches, and has been widely applied in urban management and ecological monitoring. However, in the traditional SSM methods, the spatial correlation within the multitemporal images is insufficiently exploited and is ignored in the spatiotemporal model construction. In addition, the contribution of the land covers' spatial distribution in the multitemporal images is incompletely considered, and the geographic variation during the time interval is ignored, which underutilizes the spatiotemporal information. In this paper, an SSM algorithm based on a geographically weighted regression (GWR) model and evolutionary algorithm theory, called spatiotemporal subpixel geographical evolution mapping (STGEM), is proposed for multitemporal remote sensing images. The proposed algorithm considers the spatiotemporal dependence not only between the current subpixel and the corresponding fine pixel, but also with the neighboring fine distribution patterns within the fine image. Moreover, the potential temporal information of the geospatial variation is fully realized by considering not only the time interval between the bitemporal images, but also the ratio of changed area between them, based on the GWR model. Two synthetic-image experiments with bitemporal Landsat 8 images and bitemporal QuickBird images were carried out to validate the proposed algorithm. Furthermore, a real-image experiment using a bitemporal pair of Gaofen-2 images and a Landsat 8 image was also undertaken. A comparison was made with several traditional SPM methods, as well as the state-of-the-art SSM approaches, and the experimental results confirmed the superiority of the proposed STGEM algorithm. The proposed STGEM achieves a fine spatial and temporal resolution thematic map, both qualitatively and quantitatively, and has great potential for fine-scale and frequent time-series observation and monitoring.

A Survey of Image Classification and Techniques for Improving Classification Performance

In image analysis classification of land used image is an important application. There are various algorithms used for classification of data some algorithms are rule based and some algorithms are learning based. We may get good classification but some pixels are always misclassified or unclassified. The major reason for misclassification is mixed pixel. The composition of the various objects in a single pixel makes identification of genuine class more difficult. Subpixel algorithms give the better idea about the respective class of such pixels. The subpixel mapping method is varies depending on the type of image. In Panchromatic or multispectral images the data set is very less as compared to the hyperspectral image. A hyperspectral image contains contiguous bands. Each band is very narrow with few nanometer bandwidths. More than a hundred such bands are available in the hyperspectral image. This huge data set is very difficult for the typical neural network to process. The feedforward neural network is not able to reach the local minima whereas the back propagation neural network needs a lot of time to converge to a minimum value. Radial basis function neural network has some advantages over other but it gives poor performance on hyperspectral imaging. The convolutional neural network is going to resolve the huge data problem. It has a 3-dimensional vector in which we can take multiple kernels to operate on interested data. This kernel gives us depth which is nothing but the more information of the same pixel. So here we can save a lot of information as compared to other neural networks. But in the convolutional neural network after the pooling layer, our data is in a 3D form which we need to convert again in 1D by flattening.

Contemporary Liquid Brine Exploration on Mars: From Spectral Unmixing to Subpixel Mapping

AbstractWater is a critical symbol for the existence of life in deep space exploration, and thus, the confirmation of the existence of liquid water on the contemporary Martian surface is important for the future studies of the hydrologic cycle and potential life on Mars. The main Mars observation system at a global scale is the Mars Reconnaissance Orbiter; however, this system has several drawbacks, that is, it cannot simultaneously resolve the features narrower than a few meters and scrutinize the surface composition both physically and chemically, leading to deficient and unsophisticated aqueous activity detection. Therefore, in this study, a joint endmember extraction, spectral analysis, and subpixel mapping technique, which we refer to as a joint processing pipeline, was applied to accommodate the detailed spatial information with the composition information in the physical and chemical aspects through luxuriant spectra. The validation of the proposed joint processing pipeline is described in the experimental part, with a case study of the megadune in the Russell Crater of Mars. The experimental results not only confirm the superior performance of the proposed pipeline in the accurate mapping and retrieval of Mars surface geology but also provide spectral evidence for the confirmation that hydrated material existed in the Russell Crater.

Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

一种基于模板匹配的线性特征混合像元的亚像元定位的新方法

一种基于模板匹配的线性特征混合像元的亚像元定位的新方法