A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.

Non-Negative Minimum Volume Factorization (NMVF) for Hyperspectral Images (HSI) Unmixing: A Hybrid Approach

Spectral unmixing is essential for exploitation of remotely senseddata of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members andtheir matching fractional, draft rules abundances for every pixel in HSI. Thispaper aims to unmix hyperspectral data using the minimal volume methodof elementary scrutiny. Moreover, the problem of optimization is solved bythe implementation of the sequence of small problems that are constrainedquadratically. The hard constraint in the final step for the abundance fractionis then replaced with a loss function of hinge type that accounts for outlinersand noise. Existing algorithms focus on estimating the endmembers (Ems)enumeration in a sight, discerning of spectral signs of EMs, besides assessmentof fractional profusion for every EM in every pixel of a sight. Nevertheless, allthe stages are performed by only a few algorithms in the process of hyperspectral unmixing. Therefore, the Non-negative Minimum Volume Factorization(NMVF) algorithm is further extended by fusing it with the nonnegativematrix of robust collaborative factorization that aims to perform all the threeunmixing chain steps for hyperspectral images. The major contributions ofthis article are in this manner: (A) it performs Simplex analysis of minimum volume for hyperspectral images with unsupervised linear unmixing isemployed. (B) The simplex analysis method is configured with an exaggeratedform of the elementary which is delivered by vertical component analysis(VCA). (C) The inflating factor is chosen carefully inactivating the constraintsin a large majority for relating to the source fractions abundance that speedsup the algorithm. (D) The final step is making simplex analysis method robustto outliners as well as noise that replaces the profusion element positivity hardrestraint by a hinge kind soft restraint, preserving the local minima havinggood quality. (E) The matrix factorization method is applied that is capable ofperforming the three major phases of the hyperspectral separation sequence.The anticipated approach can find application in a scenario where the endmembers are known in advance, however, it assumes that the endmemberscount is corresponding to an overestimated value. The proposed method isdifferent from other conventional methods as it begins with the overestimationof the count of endmembers wherein removing the endmembers that areredundant by the means of collaborative regularization. As demonstrated bythe experimental results, proposed approach yields competitive performancecomparable with widely used methods.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

Multiple Endmember Hyperspectral Sparse Unmixing Based on Improved OMP Algorithm

In conventional linear spectral mixture analysis model,a class is represented by a single endmember.However,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP( orthogonal matching pursuit),a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one( ANC and ASC),leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm( fully constraint OMP,FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember variability.The ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error( RMSE) to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis( s SMA),and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.

Speed-up for N-FINDR algorithm

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green canopy.This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 imagery.The tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal.Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSE<0.1),the F-scores always were≥90%whether generic endmembers or image endmembers derived information was employed.The environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.

Grain-size and compositional variability of Yarlung Tsangpo sand(Xigaze transect,south Tibet):Implications for sediment mixing by fluvial and aeolian processes

Studying the grain-size dependent compositional variability in modern river sediments provides a key to decipher the information stored in the sedimentary archive and reconstruct the evolution of the Earth’s surface in the past. Bedload sand along the Xigaze cross section of the Yarlung Tsangpo(upper Brahmaputra River) ranges in mean grain size from 0.72 Φ to 3.21 Φ, is moderately to poorly sorted and slightly platykurtic to moderately leptokurtic with sub-angular to sub-spherical grains. Litho-feldspatho-quartzose to feldspatholitho-quartzose sand(Q 43%-65%;F 13%-44%;L 11%-28%) contains 3.4%-14.4% heavy minerals including amphibole(64%-89%), epidote(4%-11%), chloritoid(0-10%), and clinopyroxene(2%-6%). The marked textural and compositional variability observed across the Xigaze transect of the Yarlung Tsangpo mainstem is controlled by both fluvial and aeolian processes, including repeated reworking by westerly and glacial winds,as well as by local contributions from northern and southern tributaries draining the Lhasa Block and the Himalayan Belt, respectively. The modern sedimentary case here will shed new light on interpreting paleogeography and provenance.

Recent advances in hyperspectral image processing

Hyperspectral images(HSI)provide a new way to exploit the internal physical composition of the land scene.The basic platform for acquiring HSI data-sets are airborne or spaceborne spectral imaging.Retrieving useful information from hyperspectral images can be grouped into four categories.(1)Classification:Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task.(2)Endmember extraction and spectral unmixing:Among images,only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the elements.Identification of endmembers from HSI thus becomes the foremost step in interpretation of each pixel.With proper endmembers,the corresponding abundances can also be exactly calculated.(3)Target detection:Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex background.Constructing a reliable rule for separating target signals from all the other background signals,even in the case of low target occurrence and high spectral variation,comprises the key to this problem.(4)Change detection:Although change detection is not a new problem,detecting changes from hyperspectral images has brought new challenges,since the spectral bands are so many,accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in HSI.In this paper,the basic theory and the most canonical works are discussed,along with the most recent advances in each aspect of hyperspectral image processing.

Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

Assessing environmental impacts of urban growth using remote sensing

This paper provides a study of the changes in land use in urban environments in two cities,Wuhan,China and western Sydney in Australia.Since mixed pixels are a characteristic of medium resolution images such as Landsat,when used for the classification of urban areas,due to changes in urban ground cover within a pixel,Multiple Endmember Spectral Mixture Analysis(MESMA)together with Super-Resolution Mapping(SRM)are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network(ANN)predicted Wavelet method.Landsat images over the two cities for a 30-year period,are classified in terms of vegetation,buildings,soil and water.The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings,vegetation,water and soil over the 30 years.The extents of fragmentation of vegetation,buildings,water and soil for the two cities are compared,while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants.Changes in Ecosystem Service Values(ESVs)resulting from the urbanization have been assessed for Wuhan and Sydney.The UN Sustainable Development Goals(SDG)for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.

Modeling diurnal variation of ground thermal radiance images using energy balance model and endmember composing technique

Modeling and analyzing dynamic changes of land thermal radiance scenes play an important role in thermal remote sensing. In this paper, the diurnal variation of ground surface thermal scene is mainly discussed. Firstly, based on the land surface energy balance equation, the diurnal variation of land surface temperatures （LSTs） over bare land covers were simulated by an analyt- ical thermal model with second harmonic terms, and the diurnal LST variation of vegetation canopy was simulated using the Cupid model. Secondly, normalized difference vegetation index （NDVI）, normalized difference water index （NDWI）, and ratio resident-area index （RRI） were used to evaluate the endmember abundance of four land cover types including vegetation, bare soil, impervious and water area, which were calculated from IKONOS visible and near infrared （VNIR） bands. Finally, the thermal radiance scenes at various times and view angles were modeled based on the linear-energy-mixing hypothesis. The re- suits showed that the simulated daily LST variations for vegetated and bare surfaces are correlated with the measured values with a maximum standard deviation of 2.7℃, that land thermal radiant textures with high-resolution are restored from the lin- ear-energy-mixing method, and that the information abundance of the scene are related to the distribution of land cover, the imaging time, and the view angle.

Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.

Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction

Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm （KNSGA）, recently developed as a nonlinear alternative to the simplex growing algorithm （SGA）, has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index （SPPI） method is used in this study to extract the first endrnember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky fac- torization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tauto- logically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.

A hyperspectral image endmember extraction algorithm based on generalized morphology

Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing(GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.

A new approach based on orthogonal bases of data space to decomposition of mixed pixels for hyperspectral imagery

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.