Spatial Attraction Model Research Articles

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

Sentinel-2 serves as a crucial data source for monitoring forest cover change. In this study, a sub-pixel mapping of forest cover is performed on Sentinel-2 images, downscaling the spatial resolution of the positioned results to 2.5 m, enabling sub-pixel-level forest cover monitoring. A novel sub-pixel mapping with edge-matching correction is proposed on the basis of the Sentinel-2 images, combining edge-matching technology to extract the forest boundary of Jilin-1 images at sub-meter level as spatial constraint information for sub-pixel mapping. This approach enables accurate mapping of forest cover, surpassing traditional pixel-level monitoring in terms of accuracy and robustness. The corrected mapping method allows more spatial detail to be restored at forest boundaries, monitoring forest changes at a smaller scale, which is highly similar to actual forest boundaries on the surface. The overall accuracy of the modified sub-pixel mapping method reaches 93.15%, an improvement of 1.96% over the conventional Sub-pixel-pixel Spatial Attraction Model (SPSAM). Additionally, the kappa coefficient improved by 0.15 to reach 0.892 during the correction. In summary, this study introduces a new method of forest cover monitoring, enhancing the accuracy and efficiency of acquiring forest resource information. This approach provides a fresh perspective in the field of forest cover monitoring, especially for monitoring small deforestation and forest degradation activities.

A Subpixel Mapping Method for Urban Land Use by Reducing Shadow Effects

Urban land use classification is significant for urban development planning. Considering complex environments of urban surface features, traditional semantic segmentation methods are difficult to solve the problems of mixed pixels and limited spatial resolution of images. The subpixel mapping technology is an effective method to solve the above problems in urban land use classification. However, traditional subpixel mapping methods are sensitive to mountain shadow, high-rise building shadow and impermeable surface heterogeneity, resulting in false classification. Therefore, we propose a subpixel mapping method that can reduce the shadow effect. This method uses a multi-index feature fusion strategy to optimize the abundance of the shadow errors in the abundance image, and uses a super-resolution reconstruction neural network model to reconstruct the optimized abundance image for the subpixel mapping of urban land use. Experiments were conducted on sentinel-2 images obtained over Yuelu District of Changsha City, Hunan Province, China. The experimental results show that the method proposed in this article can effectively overcome the influence of building shadows and mountain shadows in urban land cover classification and is superior to traditional subpixel/pixel spatial attraction model, radial basis function, super-resolution subpixel mapping, and other methods in the effect and accuracy of urban land use subpixel mapping.

Sub-Pixel Mapping of Spectral Imagery Based on Deviation Information Measurement

Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in optimal abundance images. Due to simple structure and good robustness, most SPM approaches are according to the spatial dependence assumption; namely, the closer the spatial distance is, the more likely the subpixels belong to the same class. However, the existing SPM methods based on spatial dependence assumption cannot accurately measure the DI, which affects the accuracy of the final mapping result. To address this problem, the SPM based on DI measurement (DIM) is proposed in this work. The DIM uses a dual-scale spatial attraction model (DSAM) to process the coarse abundance images to obtain predicted abundance images. The fine prior images captured at different times from the same field of view are used to measure the deviation abundance images with the DI using a geographically weighted regression (GWR) model. The predicted abundance images and deviation abundance images are fused to derive optimal abundance images. Based on the proportion information on sub-pixels being classes in the optimal abundance images, the class labels are assigned to sub-pixels by label allocation method, yielding the final mapping result. The proposed DIM method is tested on National Land-cover Dataset, Rome Dataset, and Bastrop Fires dataset. The experimental results verify that the proposed DIM achieves the best performance with the overall accuracy of 97.26%, 88.15%, and 99.80% in the three experimental results.

Subpixel Mapping for Remote Sensing Imagery Based on Spatial Adaptive Attraction Model and Conditional Random Fields

For subpixel mapping (SPM), ensuring the operational efficiency of the algorithm and mitigating the effect of abundance errors often cannot be achieved simultaneously. To solve the problem, we propose a new SPM method based on the spatial adaptive attraction model (SAAM) and conditional random fields (CRFs). First, the proposed SAAM obtains the spatial adaptive attraction value by adaptively adjusting the spatial attraction value obtained using the traditional spatial attraction model, thereby turning the display form of the abundance constraints in the SPM into an implicit form for expression, to perform the physical significance of the abundance constraints with the relative size of the attraction value of each subpixel. Second, the spatial adaptive attraction value of the implicitly represented abundance constraints and the local spatial smoothing prior are modeled in the CRFs, and the model makes full use of the spatial information in the label field while considering the abundance constraint. Third, Graph-cut is used to optimize the model, the proposed SPM can not only guarantee the operational efficiency, but also extinguish the influence of abundance error and decrease the noise artifact on the results of SPM. Experiments on three remote sensing images show that the proposed SPM accuracy is considerably better than the previously available SPM methods and is the least time-consuming. This study provides a new solution for the SPM of remote-sensing images.

Novel Multiscale Decision Fusion Approach to Unsupervised Change Detection for High-Resolution Images

High-resolution remote sensing images usually contain multiscale information, which can be used to enhance the change detection (CD) performance. How to make effective use of multiscale information needs intensive study. This letter presents a novel multiscale decision fusion (MDF) method for unsupervised CD based on Dempster–Shafer (DS) theory and modified conditional random field (CRF). The method consists of three main steps: 1) images of three different scales are created automatically by image segmentation, and then three-scale difference images (DIs) are produced by applying change vector analysis to the three-scale images; 2) the membership function of each scale DI is estimated by fuzzy clustering, and the fusion membership, as well as an initial CD map, is obtained by combining the estimated membership using DS theory; and 3) the initial CD map is refined with an improved CRF that incorporates a spatial attraction model. The proposed method can combine the multiscale information in images and the spatial contextual information. The effectiveness of the proposed method was validated by two experiments with high-resolution remote sensing images.

Super-Resolution Mapping Based on Spatial–Spectral Correlation for Spectral Imagery

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial–spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback–Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods.

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.

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.

An Advanced Superpixel-Based Markov Random Field Model for Unsupervised Change Detection

This letter proposed an advanced superpixel-based Markov random field (MRF) model for unsupervised change detection. First, simple linear iterative clustering was implemented to the difference image to get a superpixel map. The initial change map is then obtained using fuzzy $c$ -means by clustering the superpixel map. Finally, the initial change map of superpixel is iteratively refined by improved superpixel-based MRF that incorporated the spatial attraction model. It considers the spatial information of adjacent superpixels to complete the miss-detection superpixels and eliminate false-alarm superpixels. The experimental results over two data sets demonstrate the effectiveness of the proposed method.

Superresolution Land Cover Mapping Based on Pixel-, Subpixel-, and Superpixel-Scale Spatial Dependence With Pansharpening Technique

In this article, a novel superresolution mapping (SRM) method based on pixel-, subpixel-, and superpixel-scale spatial dependence (PSSSD) with pansharpening technique is proposed. First, an original coarse resolution remote sensing image and a high-resolution panchromatic image are fused by the pansharpening technique to produce a pansharpened result. A segmentation image with numerous superpixels, which represent irregular objects, is generated by adaptively segmenting the pansharpened result. Second, the class proportions of pixels, subpixels, and superpixels are, respectively, obtained from the original image, the pansharpened image, and the segmentation image. Pixel-scale spatial dependence is obtained using a pixel spatial attraction model. Subpixel-scale spatial dependence is derived using a novel subpixel spatial attraction model. Superpixel-scale spatial dependence is obtained through an extended random walker algorithm. Third, the three-scale spatial dependence is integrated into the PSSSD. Finally, a class allocation method based on object units is used to obtain the SRM results according to the PSSSD. Experimental results for three remote sensing images show that the proposed PSSSD outperforms the existing state-of-the-art SRM methods.

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 a new spatial attraction model with spatial-spectral information

ABSTRACTSubpixel mapping (SPM) is a technique for handling mixed pixels to derive hard classification map at finer spatial resolution. Due to simplicity and explicit physical meanings, spatial attraction model (SAM) has been proven to be an effective SPM method. SAM methods mostly differ in the way in which they compute the spatial attraction, for example, subpixel/pixel spatial attraction model (SPSAM), subpixel/subpixel spatial attraction model (MSPSAM), and the mixed spatial attraction model (MSAM). However, these methods are difficult to fully utilize the spatial-spectral information of the original remote sensing image due to diversity of the land cover classes and the limitation of the resolution of the sensor. To solve this problem, spatial attraction model with spatial-spectral information (SAM-SS) is proposed here. SAM-SS can pick up more spatial-spectral information of the original image by adding a new processing path, namely interpolation followed by spectral unmixing. Based on visual comparison and quantitative accuracy assessment, the SAM-SS shows the best performance in the four experimental results.

Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique

The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial–spectral information from the original image. To utilize more spatial–spectral information, improving remote sensing image super-resolution mapping based on the spatial attraction model by utilizing the pansharpening technique (SRMSAM-PAN) is proposed. In SRMSAM-PAN, a novel processing path, named the pansharpening path, is added to the existing SRMSAM. The original coarse remote sensing image is first fused with the high-resolution panchromatic image from the same area by the pansharpening technique in the novel pansharpening path, and the improved image is unmixed to obtain the novel fine-fraction images. The novel fine-fraction images from the pansharpening path and the existing fine-fraction images from the existing path are then integrated to produce finer-fraction images with more spatial–spectral information. Finally, the values predicted from the finer-fraction images are utilized to allocate class labels to all subpixels, to achieve the final mapping result. Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods.

Large-scale subpixel mapping of landcover from MODIS imagery using the improved spatial attraction model

Mapping plastic-mulched landcover (PML) is an important agricultural monitoring task. Because of its daily revisiting, imagery from moderate-resolution imaging spectroradiometer (MODIS) has been widely used to detect PML over a large area. However, the coarse spatial resolution and small field size make subpixel PML a problem for accurate PML mapping from MODIS. This study applies the improved spatial attraction model (ISAM), which estimates the spatial attraction of the central subpixel of a moving window by all subpixels in the window, to map large-scale subpixel PML from MODIS imagery. The linear spectral mixing model is used to obtain fractions of PML and three other landcover classes in each MODIS pixel as the inputs to ISAM for obtaining the hard subpixel PML maps at spatial resolution of 31.25 m. The accuracy evaluation, with validated landcover classification derived from Landsat-8 imagery as the ground truth, shows that overall accuracy, Kappa coefficients, producer accuracy, and user accuracy are 83.51%, 0.69, 90.97%, and 81.51%, respectively, indicating large-scale PML mapping at spatial resolution comparable to Landsat-8 can be derived from MODIS images with acceptable accuracy by ISAM. This study provides a practical and economic way for mapping PML for a large area at ∼30-m spatial resolution.

Enhanced Spatially Constrained Remotely Sensed Imagery Classification Using a Fuzzy Local Double Neighborhood Information C-Means Clustering Algorithm

This paper presents a fuzzy local double neighborhood information ${\text{c}}$ -means (FLDNICM) clustering algorithm for remotely sensed imagery classification, which incorporates flexible and accurate local spatial and spectral information. First, a trade-off weighted fuzzy factor is established based on a pixel spatial attraction model that considers spatial distance and class membership differences between the central pixel and its neighbor simultaneously. This factor can adaptively and accurately estimate the spatial constraints from neighboring pixels. To further enhance robustness to noise and outliers, another fuzzy prior probability function is also defined, which integrates the mutual dependence information from a pixel and its neighbor in a fuzzy logical way for obtaining accurate spatial contextual information. The FLDNICM enhances the conventional fuzzy ${\text{c}}$ -means algorithm by producing homogeneous segmentation while reducing the edge blurring artifacts. The new trade-off weighted fuzzy factor and prior probability function are both parameter free and fully adaptive to the image content. Experimental results demonstrate the superiority of FLDNICM over competing methodologies, considering a series of synthetic and real-world images classification applications.

An Improved Subpixel Mapping Algorithm Based on a Combination of the Spatial Attraction and Pixel Swapping Models for Multispectral Remote Sensing Imagery

To obtain spatial feature distributions from the mixed pixels of remote sensing images and increase the accuracy of land-cover classification and recognition, a double-calculated spatial attraction model (DSAM) based on the combination of spatial attraction model (SAM) and pixel swapping model (PSM) is presented and verified by introducing the law of universal gravitation to describe the attraction between pixels. In DSAM, SAM was used to improve the initialization algorithm of PSM, and the optimization algorithm of PSM was improved accordingly. Using a SPOT-5 remote sensing image, related subpixel mapping (SPM) experiments were performed to verify the SPM effect of DSAM and to test its accuracy. The experimental results indicated that the DSAM SPM results were superior to the SPM results of SAM and PSM. DSAM was proven effective and applicable for the SPM of remotely sensed images, and the model can improve the accuracy of SPM and landcover classification.

Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data

Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting subpixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water-Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier’s performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.

Influence of reconstruction scale, spatial resolution and pixel spatial relationships on the sub-pixel mapping accuracy of a double-calculated spatial attraction model

Mixed pixels universally exist in remote sensing images, and they are one of the main obstacles for further improving the accuracy of land cover recognition and classification. Since the concept of sub-pixel mapping (SPM) is proposed, SPM technology has rapidly become an important method to solve the problem of mixed pixels. To further improve SPM accuracy, this paper first proposes a double-calculated spatial attraction model (DSAM) combining the advantages of the spatial attraction model (SAM) and the pixel swap model (PSM). Then, based on the full validation of the proposed DSAM, how multiple factors affect the SPM accuracy is analyzed using the multispectral remote sensing (MRS) images. Finally, by analyzing the maximum variations in the ranges of the overall accuracy and the kappa coefficient under different multiple factors, the order of factors influencing SPM accuracy is determined as follows: reconstruction scale > image spatial resolution > pixel spatial relationships. The results can serve as a reference for other scholars in setting model parameters and selecting the appropriate remote sensing data, thereby helping them achieve more accurate SPM results.

A Hybrid Subpixel Mapping Framework for Hyperspectral Images Using Collaborative Representation

Subpixel mapping with a low-resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image is not always available in practice. In this paper, the collaborative representation-based subpixel mapping (CRSPM) framework is proposed to acquire an improved classification map at subpixel scale with only a low-resolution hyperspectral image available. To efficiently extract and utilize spatial information in this case without auxiliary image, the low-resolution hyperspectral (LHS) image is processed in a hybrid framework in two different ways to generate two subpixel scale classification maps. One is obtained by classifying the upsampled LHS image using collaborative representation-based (CR-based) classifier. The other is available using CR-based classification combined with spectral unmixing and subpixel spatial attraction model. Specifically, to enclose the contextual spatial information for higher classification accuracy, a spatially joint as well as post-partitioning CR-based classifier, JCRT-based classifier, is proposed and applied in this work. To achieve better classification performance, decision fusion is applied to determine class label from the two classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed CRSPM approach clearly outperforms some state-of-the-art subpixel mapping approaches by producing smoother classification map with less misclassification.

Morphometric analysis of stream as one of resources for agricultural lands irrigation using high spatial resolution of digital elevation model (DEM)

The aim of the study in this area is the extraction of streams using a sub-pixel spatial attraction model as a new method for increasing spatial resolution of digital elevation model (DEM) in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel to sub-pixels based on the fraction values in neighboring pixels that can be attracted only by a central pixel. Based on this approach only a maximum of eight neighboring pixels can be selected for the attraction. In the model, other pixels are supposed to be far from the central pixel to have any attraction. In this study, the spatial resolution of digital elevation models (DEM) was increased by using sub-pixel attraction model. The design of the algorithm is accomplished through using digital elevation model (DEM) with spatial resolution of 30m (Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) and 90m (Shuttle Radar Topography Mission (SRTM)) in the north of Darab, Fars province, Iran. In attraction model scale factors of (2,3,4) with two neighboring methods of touching and quadrant are applied to DEMs using Matlab. The algorithm is evaluated using 487 sample points that are measured by researchers. As the result, it was revealed that the spatial attraction model with scale factor of (S=2) gives better results compared to scale factors that are greater than 2 and also touching neighborhood method proved to be more accurate then quadrant. In fact, subtracting each pixel to more than two sub-pixels caused to the decrease of the accuracy of resulted DEM which leads to the increase of the value of root-mean-square error (RMSE) and showed that attraction models could not be used for S which is greater than 2. So according to the results, it is suggested that the model can be used for increasing spatial resolution of DEM in the studies catchment.