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New Advances on Sub-pixel Processing: Unmixing and Mapping Methods

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 33892

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Special Issue Editors

Dr. Addisson Salazar

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Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Interests: pattern recognition; signal processing on graphs; dynamic modeling; decision fusion; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Luis Vergara

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Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Interests: statistical signal processing; pattern recognition; machine learning; graph signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Gonzalo Safont

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Guest Editor

Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: independent component analysis; signal processing; pattern recognition; classification methods; image processing; decision fusion; biosignals; remote sensing

Special Issue Information

Dear Colleagues,

Sub-pixel processing takes into account the possibility of a pixel to belong to different classes in an image segmentation context. This is especially relevant in remote sensing, where different macroscopic or microscopic components could appear to contribute to every pixel. Thus sub-pixel segmentation can increase the resolution of the original images, which is limited by the sensorial systems and several measurement effects. Two main problems are identified in sub-pixel processing: unmixing and mapping. Unmixing refers to the separation of the components contributing to the pixel information (typically a pixel signature obtained from hyperspectral images) to build abundance maps describing the proportion of every component in a given pixel. Mapping is the distribution inside a pixel of its labelled sub-pixels, consistently with the abundance maps.

A good variety of options have been proposed so far, but there is still room for new advances in this challenging problem. Thus nonlinear decomposition techniques like Independent Component Analysis, Bounded Component Analysis or Nonnegative Matrix Factorization, among others, are candidates to improve conventional unmixing methods based on linear models like Principal Component Analysis. On the other hand, sub-pixel mapping is an ill-conditioned problem as far as many possible sub-pixel distributions are compatible with the estimated abundance maps. Advanced solutions from machine learning and pattern analysis and recognition have been also devised to solve the inherent problems of sub-pixel processing. This is symptomatic of the very diverse approaches adopted and highlights the potential of research in this area.

Thus the aim of this special issue is to contribute with new theoretical and practical insights in this booming topic focused to the remote sensing imagery.

Dr. Addisson Salazar
Prof. Luis Vergara
Dr. Gonzalo Safont
Guest Editors

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Keywords

Pixel swapping model (PSM)
Spatial attraction model (SAM)
Spatial correlation
Sub-pixel mapping (SPM)
Hyper spectral images
Artificial neural networks
Deep Learning
Conventional Neural Networks
Image interpolation
Image classification
Sub-pixel change detection
Pattern analysis and recognition
Classification methods
Independent component analysis
Principal component analysis
Bounded component analysis

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Published Papers (10 papers)

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Editorial

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2 pages, 169 KiB

Open AccessEditorial

Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”

by Addisson Salazar, Luis Vergara and Gonzalo Safont

Remote Sens. 2021, 13(19), 3807; https://doi.org/10.3390/rs13193807 - 23 Sep 2021

Viewed by 1443

Abstract

Innovative remote sensing image processing techniques have been progressively studied due to the increasing availability of remote sensing images, powerful techniques of data analysis, and computational power [...] Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

Research

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18 pages, 5621 KiB

Open AccessArticle

Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary

by Bouthayna Msellmi, Daniele Picone, Zouhaier Ben Rabah, Mauro Dalla Mura and Imed Riadh Farah

Remote Sens. 2021, 13(2), 190; https://doi.org/10.3390/rs13020190 - 7 Jan 2021

Cited by 5 | Viewed by 2915

Abstract

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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22 pages, 538 KiB

Open AccessArticle

Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

by José Prades, Gonzalo Safont, Addisson Salazar and Luis Vergara

Remote Sens. 2020, 12(21), 3585; https://doi.org/10.3390/rs12213585 - 1 Nov 2020

Cited by 25 | Viewed by 2740

Abstract

Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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23 pages, 1159 KiB

Open AccessArticle

Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability

by Tatsumi Uezato, Mathieu Fauvel and Nicolas Dobigeon

Remote Sens. 2020, 12(14), 2326; https://doi.org/10.3390/rs12142326 - 20 Jul 2020

Cited by 7 | Viewed by 3336

Abstract

Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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17 pages, 3537 KiB

Open AccessArticle

Spatial Attraction Models Coupled with Elman Neural Networks for Enhancing Sub-Pixel Urban Inundation Mapping

by Linyi Li, Yun Chen, Tingbao Xu, Lingkui Meng, Chang Huang and Kaifang Shi

Remote Sens. 2020, 12(13), 2068; https://doi.org/10.3390/rs12132068 - 27 Jun 2020

Cited by 14 | Viewed by 2666

Abstract

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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19 pages, 6088 KiB

Open AccessArticle

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

by Jie Kong, Quansen Sun, Mithun Mukherjee and Jaime Lloret

Remote Sens. 2020, 12(7), 1164; https://doi.org/10.3390/rs12071164 - 4 Apr 2020

Cited by 12 | Viewed by 2946

Abstract

As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l_2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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20 pages, 4927 KiB

Open AccessArticle

Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data

by Chenzhou Liu, Jiancheng Shi, Xiuying Liu, Zhaoyong Shi and Ji Zhu

Remote Sens. 2020, 12(7), 1154; https://doi.org/10.3390/rs12071154 - 3 Apr 2020

Cited by 15 | Viewed by 3100

Abstract

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R²) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%). Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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21 pages, 7816 KiB

Open AccessArticle

Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels

by Xiaodong Li, Rui Chen, Giles M. Foody, Lihui Wang, Xiaohong Yang, Yun Du and Feng Ling

Remote Sens. 2020, 12(3), 503; https://doi.org/10.3390/rs12030503 - 4 Feb 2020

Cited by 6 | Viewed by 3954

Abstract

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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18 pages, 5777 KiB

Open AccessArticle

Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

by Peng Wang, Lei Zhang, Gong Zhang, Benzhou Jin and Henry Leung

Remote Sens. 2019, 11(22), 2695; https://doi.org/10.3390/rs11222695 - 18 Nov 2019

Cited by 8 | Viewed by 2975

Abstract

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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17 pages, 6323 KiB

Open AccessArticle

Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

by Yuanxin Jia, Yong Ge, Yuehong Chen, Sanping Li, Gerard B.M. Heuvelink and Feng Ling

Remote Sens. 2019, 11(15), 1815; https://doi.org/10.3390/rs11151815 - 2 Aug 2019

Cited by 40 | Viewed by 5816

Abstract

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (

{SRM}_{CNN}

) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed

{SRM}_{CNN}

method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed

{SRM}_{CNN}

method was validated by visualizing output features and analyzing the performance of different geographic objects. Full article

(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)

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