Sub-pixel Mapping Research Articles

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.

Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping

Capturing the dynamics of a lake-water area using remotely sensed images has always been an essential task. Most of the fine spatial resolution data are unsuitable for this purpose because of their low temporal resolution and limited scene coverage. A Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP–VIIRS) is a newly-available and appropriate sensor for monitoring large lakes due to its frequent revisits and wide swath (more than 3000 km). However, it provides visible and infrared images at relatively coarse spatial resolutions, which would sometimes hamper the accurate mapping of lake shorelines. This study, therefore, proposes a two-step downscaling method that combines spectral unmixing and subpixel mapping to produce a finer resolution lake map from NPP–VIIRS imagery, which is then applied to delineate the shorelines of five plateau lakes in Yunnan Province, as well as the shoreline dynamics of Poyang Lake at three separate times. A newly published global water dynamic dataset is employed in this study to improve the downscaling method. Results suggest that the proposed method can generate a finer resolution lake map that exhibits more details of the shoreline than hard classification. The downscaling results of the Suomi NPP–VIIRS generally achieve higher than 75% accuracy, while the downscaling results of a Landsat-simulated fraction map could have accuracy higher than 85%. This reveals that errors and uncertainties exist in both procedures, but mainly come from the spectral unmixing procedure which retrieves water fractions from NPP–VIIRS data.

A Novel Approach to Subpixel Land-Cover Change Detection Based on a Supervised Back-Propagation Neural Network for Remotely Sensed Images With Different Resolutions

Extracting subpixel land-cover change detection (SLCCD) information is important when multitemporal remotely sensed images with different resolutions are available. The general steps are as follows. First, soft classification is applied to a low-resolution (LR) image to generate the proportion of each class. Second, the proportion differences are produced by the use of another high-resolution (HR) image and used as the input of subpixel mapping. Finally, a subpixel sharpened difference map can be generated. However, the prior HR land-cover map is only used to compare with the enhanced map of LR image for change detection, which leads to a nonideal SLCCD result. In this letter, we present a new approach based on a back-propagation neural network (BPNN) with a HR map (BPNN_HRM), in which a supervised model is introduced into SLCCD for the first time. The known information of the HR land-cover map is adequately employed to train the BPNN, whether it predates or postdates the LR image, so that a subpixel change detection map can be effectively generated. In order to evaluate the performance of the proposed algorithm, it was compared with four state-of-the-art methods. The experimental results confirm that the BPNN_HRM method outperforms the other traditional methods in providing a more detailed map for change detection.

Sub-pixel classification of hydrothermal alteration zones using a kernel-based method and hyperspectral data; A case study of Sarcheshmeh Porphyry Copper Mine and surrounding area, Kerman, Iran

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

AbstractSatellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth’s environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

Coupling the Modified Linear Spectral Mixture Analysis and Pixel-Swapping Methods for Improving Subpixel Water Mapping: Application to the Pearl River Delta, China

High-resolution water mapping with remotely sensed data is essential to monitoring of rainstorm waterlogging and flood disasters. In this study, a modified linear spectral mixture analysis (LSMA) method is proposed to extract high-precision water fraction maps. In the modified LSMA, the pure water and mixed water-land pixels, which are extracted by the Otsu method and a morphological dilation operation, are used to improve the accuracy of water fractions. The modified LSMA is applied to the 18 October 2015 Landsat 8 OLI image of the Pearl River Delta for the extraction of water fractions. Based on the water fraction maps, a modified subpixel mapping method (MSWM) based on a pixel-swapping algorithm is proposed for obtaining the spatial distribution information of water at subpixel scale. The MSWM includes two steps in subpixel water mapping. The MSWM considers the inter-subpixel/pixel and intra-subpixel/subpixel spatial attractions. Subpixel water mapping is first implemented with the inter-subpixel/pixel spatial attractions, which are estimated using the distance between a given subpixel and its surrounding pixels and the water fractions of the surrounding pixels. Based on the initialized subpixel water mapping results, the final subpixel water maps are determined by a modified pixel-swapping algorithm, in which the intra-subpixel/subpixel spatial attractions are estimated using the initialized subpixel water maps and an inverse-distance weighted function of the current subpixel at the centre of a moving window with its surrounding subpixels within the window. The subpixel water mapping performance of the MSWM is compared with that of subpixel mapping for linear objects (SPML) and that of the subpixel/pixel spatial attraction model (SPSAM) using the GF-1 reference image from 20 October 2015. The experimental results show that the MSWM shows better subpixel water mapping performance and obtains more details than SPML and SPSAM, as it has the largest overall accuracy values and Kappa coefficients. Furthermore, the MSWM can significantly eliminate the phenomenon of jagged edges and has smooth continuous edges.

Subpixel mapping and test beam studies with a HV2FEI4v2 CMOS-Sensor-Hybrid Module for the ATLAS inner detector upgrade

The upgrade to the High Luminosity Large Hadron Collider will increase the instantaneous luminosity by more than a factor of 5, thus creating significant challenges to the tracking systems of all experiments. Recent advancement of active pixel detectors designed in CMOS processes provide attractive alternatives to the well-established hybrid design using passive sensors since they allow for smaller pixel sizes and cost effective production. This article presents studies of a high-voltage CMOS active pixel sensor designed for the ATLAS tracker upgrade. The sensor is glued to the read-out chip of the Insertable B-Layer, forming a capacitively coupled pixel detector. The pixel pitch of the device under test is 33× 125 μm2, while the pixels of the read-out chip have a pitch of 50× 250 μm2. Three pixels of the CMOS device are connected to one read-out pixel, the information of which of these subpixels is hit is encoded in the amplitude of the output signal (subpixel encoding). Test beam measurements are presented that demonstrate the usability of this subpixel encoding scheme.

Optimizing Subpixel Impervious Surface Area Mapping Through Adaptive Integration of Spectral, Phenological, and Spatial Features

Reliable subpixel impervious surface area (ISA) mapping at medium resolution is essential but difficult due to the complexity of land cover patterns within the urban/peri-urban area. In this letter, we proposed a framework to optimize subpixel ISA mapping performance with adaptive integration of features from spectral, phenological, and spatial dimensions. We utilized the recursive feature elimination to build the most discriminative feature pool. Then, the random forest (RF) model was adopted for the subpixel ISA mapping and the feature contribution quantification. We applied the proposed framework in two typical study sites and tested its utility by comparing it with three other subpixel mapping approaches. The experimental results suggested that the inclusion of complementary feature inputs beyond the spectral profile was beneficial in both study sites for identifying fractional imperviousness. In particular, the improvement was most pronounced for pixels suffering spectral variability or intra-annual land cover change. With the quantification of feature contribution to the RF model, we further illustrated the critical impact of environmental conditions on the feature adoption.

Subpixel area-based evaluation for crosstalk suppression in quasi-three-dimensional displays.

A subpixel area-based evaluation method for an improved slanted lenticular film that minimizes the crosstalk in a quasi-three-dimensional (Q3D) display is proposed in this paper. To identify an optimal slant angle of the film, a subpixel area-based measurement is derived to evaluate the crosstalk among viewing regions of the intended subpixel and adjacent unintended subpixel by taking the real subpixel shape and black matrix into consideration. The subpixel mapping, which corresponds to the optimal slant angle of the film, can then be determined. Meanwhile, the viewing zone characteristics are analyzed to balance the light intensity in both right and left eye channels. A compact and portable Q3D system has been built and appropriate experiments have been applied. The results indicate that significant improvements in both crosstalk and resolution can be obtained with the proposed technique.

Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy.

Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments.

Subpixel Change Detection of Multitemporal Remote Sensed Images Using Variability of Endmembers

Due to the existence of mixed pixels in a remote sensed image, traditional change detection (CD) methods at “full-pixel level” are often unable to provide detailed changed information effectively. A subpixel change detection (SCD) technique can deal with this issue with two steps: soft classification is applied to derive proportional differences from coarse multitemporal images, and then a sharpened thematic map with fine spatial resolution is generated based on subpixel mapping. However, changes in endmember combination within pixels are ignored, which can result in flawed differences and degraded accuracy of SCD. The aim of this letter is to present a new SCD algorithm using variability of endmembers (SCD_VE), where a simple but effective model is proposed to take into consideration the real change of endmember combination. In order to evaluate the performance of the new algorithm, experiment is conducted on simulated images. Experimental results demonstrated that the proposed SCD_VE offers better performance than traditional SCD methods in providing more detailed CD map.

Using Multiple Subpixel Shifted Images With Spatial–Spectral Information in Soft-Then-Hard Subpixel Mapping

Multiple subpixel shifted images (MSIs) from the same area can be incorporated to improve the accuracy of soft-then-hard subpixel mapping (STHSPM). In this paper, a novel method that derives higher resolution MSIs with more spatial–spectral information (MSI-SS) is proposed. First, coarse MSIs produce two high-resolution MSIs for each class respectively by two paths at the same time. The spatial path produces the high-resolution MSIs by soft classification followed by interpolation. And, the other high-resolution MSIs are derived from the spectral path by interpolation followed by soft classification. Then the higher resolution MSIs with more spatial–spectral information for each class are derived by integrating the aforementioned two kinds of high-resolution MSIs by the appropriate weight. Finally, the integrated higher resolution MSIs for each class are used to allocate hard class labels to subpixels. The proposed method is fast and takes more spatial–spectral information of the original MSIs into account. Experiments on three real hyperspectral remote sensing images show that the proposed method produce higher SPM accuracy result.

Isolating type-specific phenologies through spectral unmixing of satellite time series

ABSTRACTVegetation phenology is commonly studied using time series of multi-spectral vegetation indices derived from satellite imagery. Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing, and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution. We present an alternative method to mitigate this ‘mixed-pixel problem’ and extract the phenological behavior of individual land-cover types inferentially, by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping. Parameterized using genetic algorithms, the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red, near infrared, and short-wave infrared wavelengths, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index. In simulation, the unmixing procedure reproduced the reflectances and phenological signals of grass, crop, and deciduous forests with high fidelity (RMSE < 0.007 NDVI); and in empirical tests, the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah. The approach shows potential for a wide range of ecological applications, including detection of differential responses to climate, soil, or other factors among vegetation types.

Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support Vector Regression

Hyperspectral remote sensing data offer the opportunity to map urban characteristics in detail. Though, adequate algorithms need to cope with increasing data dimensionality, high redundancy between individual bands, and often spectrally complex urban landscapes. The study focuses on subpixel quantification of urban land cover compositions using simulated environmental mapping and analysis program (EnMAP) data acquired over the city of Berlin, utilizing both machine learning regression and classification algorithms, i.e., multioutput support vector regression (MSVR), standard support vector regression (SVR), import vector machine classifier (IVM), and support vector classifier (SVC). The experimental setup incorporates a spectral library and a reference land cover fraction map used for validation purposes. The library spectra were synthetically mixed to derive quantitative training data for the classes vegetation, impervious surface, soil, and water. MSVR and SVR models were trained directly using the synthetic mixtures. For IVM and SVC, a modified hyperparameter selection approach is conducted to improve the description of urban land cover fractions by means of probability outputs. Validation results demonstrate the high potential of the MSVR for subpixel mapping in the urban context. MSVR outperforms SVR in terms of both accuracy and computational time. IVM and SVC work similarly well, yet with lower accuracies of subpixel fraction estimates compared to both regression approaches.

A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

Subpixel mapping (SPM) is a technique that produces hard classification maps at a spatial resolution finer than that of the input images produced when handling mixed pixels. Existing spatial attraction model (SAM) techniques have been proven to be an effective SPM method. The techniques mostly differ in the way in which they compute the spatial attraction, for example, from the surrounding pixels in the subpixel/pixel spatial attraction model (SPSAM), from the subpixels within the surrounding pixels in the modified SPSAM (MSPSAM), or from the subpixels within the surrounding pixels and the touching subpixels within the central pixel in the mixed spatial attraction model (MSAM). However, they have a number of common defects, such as a lack of consideration of the attraction from subpixels within the central pixel and the unequal treatment of attraction from surrounding subpixels of the same distance. In order to overcome these defects, this study proposed an improved SAM (ISAM) for SPM. ISAM estimates the attraction value of the current subpixel at the center of a moving window from all subpixels within the window, and moves the window one subpixel per step. Experimental results from both Landsat and MODIS imagery have proven that ISAM, when compared with other SAMs, can improve SPM accuracies and is a more efficient SPM technique than MSPSAM and MSAM.

Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment

Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been obvious that the utilized abundance map has a strong impact on the subsequent subpixel mapping procedure. However, limited attention has been given to the impact of the different aspects in the spectral unmixing model on the subpixel mapping performance. In this paper, a detailed quantitative assessment of different aspects in linear spectral mixture analysis, such as the criteria used to determine the types of pixels, the abundance sum-to-one constraint in the unmixing, and the accuracy of the utilized abundance maps, is investigated. This is accomplished by designing an experimental procedure with replaceable components. A total of six hyperspectral images (four synthetic and two real) were utilized in our experiments. By investigating these critical issues, we can further improve the performance of subpixel mapping techniques.

Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression

Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying urban land cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative urban mapping.

Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions

Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Soft classification (SC) can produce the proportional fractions of land-covers, on which sub-pixel mapping (SPM) can construct fine resolution land-cover maps to reduce the low-spatial-resolution-problem to some extent. Thus, in this paper, sub-pixel land-cover change detection with the use of different resolution images (SLCCD_DR) is addressed based on SC and SPM. Previously, endmember combinations within pixels are ignored in the LR image, which may result in flawed fractional differences. Meanwhile, the information of a known HR land-cover map is insignificantly treated in the SPM models, which leads to a reluctant SLCCD_DR result. In order to overcome these issues, a novel approach based on a back propagation neural network (BPNN) with different resolution images (BPNN_DR) is proposed in this paper. Firstly, endmember variability per pixel is considered during the SC process to ensure the high accuracy of the derived proportional fractional difference image. After that, the BPNN-based SPM model is constructed by a complete supervised framework. It takes full advantage of the prior known HR image, whether it predates or postdates the LR image, to train the BPNN, so that a sub-pixel change detection map is generated effectively. The proposed BPNN_DR is compared with four state-of-the-art methods at different scale factors. The experimental results using both synthetic data and real images demonstrated that it can outperform with a more detailed change detection map being produced.

The effect of the point spread function on sub-pixel mapping

Sub-pixel mapping (SPM) is a process for predicting spatially the land cover classes within mixed pixels. In existing SPM methods, the effect of point spread function (PSF) has seldom been considered. In this paper, a generic SPM method is developed to consider the PSF effect in SPM and, thereby, to increase prediction accuracy. We first demonstrate that the spectral unmixing predictions (i.e., coarse land cover proportions used as input for SPM) are a convolution of not only sub-pixels within the coarse pixel, but also sub-pixels from neighboring coarse pixels. Based on this finding, a new SPM method based on optimization is developed which recognizes the optimal solution as the one that when convolved with the PSF, is the same as the input coarse land cover proportion. Experimental results on three separate datasets show that the SPM accuracy can be increased by considering the PSF effect.

Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning

Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.