Spectral Mixture Model Research Articles

Application of Harmonic Analysis and Linear Spectral Mixture Model to Extract the Cultivated Resource

Application of Harmonic Analysis and Linear Spectral Mixture Model to Extract the Cultivated Resource

Spectral mapping of morphological features on the moon with MGM and SAM

Three types of morphological features observed in different lunar crustal terrains were studied and mapped using hyperspectral Moon mineralogy mapper (M3) data onboard Chandrayaan 1 mission in order to assess the utility of cascaded MGM-SAM spectral mixture modeling approach to characterize the surface materials, which may occur as mineral mixtures, at different topography of the lunar surface. Selected morphological features include: the impact melts in Orientale basin, sinuous rilles in Procellarum KREEP Terrane (PKT) and a rayed crater in Feldspathic Highland Terrane (FHT). Methodology involves extraction of spectrally pure pixels (endmembers) of the area using Pixel Purity Index (PPI), identification of mineralogy of the selected endmember spectrum using the Modified Gaussian Method (MGM) and mapping of mineralogically identified endmembers using the Spectral Angle Mapper (SAM) method.Mapping results demonstrate both the capabilities and the limitations of the MGM method of spectral deconvolution and the SAM method of spectral matching as effective tools for compositional characterizations of morphological features on the lunar surface. As a method of spectral deconvolution, MGM was able to identify and characterize both high- and low – Ca pyroxenes along with plagioclase feldspar. The Spectral Angle Mapper (SAM) was able to map identified mineral mixtures from MGM.

Projecting the Urban Future: Contributions from Remote Sensing

Mapping urban extent accurately with remotely sensed imagery is challenging. In addition to the inherent difficulty of defining urban land use in terms of land cover (LC) at fixed spatial scales, robust identification of LC using remote sensing has its own challenges. Many of these challenges are related to the fact that the physical properties and conditions that influence scattering and emission of radiation vary in time, space, geography, geometry and wavelength. A second set of difficulties arises in the process of discretizing multiple scales of LC that often vary continuously in abundance over the range of spatial scales at which sensors integrate responses into individual pixels. These factors underscore the importance of spatial and spectral resolution for mapping human settlements and urban extent. The evolution of urban remote sensing is discussed in the context of changes in spatial and spectral resolution and in the tools used to classify land cover. A multi-sensor approach to urban extent mapping acknowledges the limitations of individual sensors and uses the intersection of multiple physical criteria to distinguish the built environment from other types of land cover. A simple example of multi-sensor mapping combines decameter-scale reflectance, derived from Landsat imagery, with kilometer-scale emission of night light, derived from DMSP-OLS composites. Current availability of intercalibrated, accurately co-registered Landsat imagery extending back to the 1970s allows for retrospective analyses of a variety of human modified landscapes—including urban. Combined with continuous field representation of land cover at subpixel scales, many of the limitations of single image discrete classification can be avoided. Global availability of Landsat allows for comparative analyses of human settlements in different environments and cultures. To facilitate comparison in terms of physical properties of land cover, we use a standardized spectral mixture model to convert the Landsat surface reflectance spectra to areal abundances of Substrate, Vegetation and Dark fractions. Because many land uses result in time-varying land cover properties we characterize the Landsat response across multiple seasons within a single year in terms of the seasonal mean and variability of each spectral endmember. The results of the multi-sensor characterization quantify both consistencies and inconsistencies in the combined response of modified landscapes in a variety of environments worldwide.

Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation

This paper investigates the retrieval of forest crown closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model.

Modeling images with motion error scanned by a temporally and spatially modulated Fourier-transform imaging spectrometer

Applied to remote sensing, a temporally and spatially modulated Fourier-transform imaging spectrometer (TSMFTIS) relies on the push broom of the flying platform to obtain an interferogram of the detected target. If the moving state of the flying platform changes during the imaging process, the target interferogram picked up from the remotely sensed image sequence will deviate from the ideal interferogram, and the recovered target spectrum will not reflect the real characteristics of the ground target object. On the basis of research on TSMFTISs, the coordinate relationship between the detector pixel and the corresponding target ground object in the photogrammetric coordinate system can be precisely described on the basis of the central projection imaging collinear equation, and the motion imaging error model of the TSMFTIS is derived using a linear spectral mixture model. The TSMFTIS motion imaging process is simulated by an actual spectrum data cube and the altitude of the airborne platform, and a sequence error image with interference fringes is acquired. Finally, an experiment is performed to validate the motion imaging error model using the TSMFTIS and a motorized stage, and the evaluation results demonstrate its feasibility.

Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model

Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.

Use of Applied Geotechnology for Analysis of the Permanent Preservation Area - APP of Bico da Pedra Lake, in the Municipality of Janaúba, MG

Urbanization in permanent preservation areas (APP) results in eutrophication of water bodies, water contamination and change in gene flow of the fauna and flora. Given the above, the purpose of this study was to analyze the permanent preservation areas of the Bico da Pedra Lake in the municipality of Janaúba, MG, using geotechnology. The methodology consisted of the use of Linear Spectral Mixture Model (LSMM) and the Bhattacharya unsupervised classification developed from the image of the Landsat-5TM satellite to identify the land use and vegetation in the Lake’s APP. Subsequently, we scanned the QuickBird satellite image at a constant scale of 1:5000 with the purpose of measuring the built-up areas located in the APP in the analysis. The results identified the presence of exposed soil and vegetation suppression in certain locations in the APP. The quantitative area of the APP was measured at 1768.89 ha, with the presence of 15% exposed soil, 32% sparse vegetation, 52% dense vegetation, and 1% non-classified area. We performed scanning for the presence of masonry buildings and farms; 79 of which are in the maximum level of flooding and 217 are in the APP. We concluded that the study obtained satisfactory results for the analysis and measurement of the area of permanent preservation area, allowing the identification of targets and vegetation accurately. Thus, it is inferred that this methodology can be applied in permanent preservation areas in separate locations aiming at monitoring and environmental supervision.

Monitoring urban impervious surface area change using China-Brazil Earth Resources Satellites and HJ-1 remote sensing images

Impervious surface area (ISA) plays an important role in monitoring urbanization and related environmental changes, and has become a hotspot in urban and environmental studies. Xuzhou City, located in northwest Jiangsu Province, China, is chosen as the study area, and two scenes of China-Brazil Earth Resources Satellites images and one scene of HJ-1 image are employed to estimate ISA percentage and analyze the change trend from 2001 to 2009. Using a linear spectral mixture model (LSMM) and nonlinear backpropagation neural network (BPNN) method, all pixels are decomposed to derive four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects, and soil. The ISA percentage is then derived by the combination of high- and low-albedo fraction images after removing the influence of water. Some high spatial resolution images are selected to validate the ISA estimation results, and the experimental results indicate that the accuracy of BPNN is higher than LSMM. By comparing the urban ISA abundances derived by BPNN from three dates, it is found that the ISA of Xuzhou City has increased rapidly from 2001 to 2009, especially in the northeast and southeast regions, corresponding to the urban planning scheme and fast urbanization. Compared to other medium remote sensing images, the revisit cycle of HJ-1 multispectral image is only two days, demonstrating the potential of such data for ISA extraction in urbanization, disaster, and other related applications.

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1 for PF versus 98.5 ± 21.4 ton ha−1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.

基于LSMM与MSPA的深圳市绿色景观连通性研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于LSMM与MSPA的深圳市绿色景观连通性研究 DOI: 10.5846/stxb201306101563 作者: 作者单位: 北京市海淀区房屋管理局,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金青年基金(41101175);国家自然科学面上基金(41171440)资助 Landscape connectivity dynamics of urban green landscape based on morphological spatial pattern analysis (MSPA) and linear spectral mixture model (LSMM) in Shenzhen Author: Affiliation: Haidian Real Estate Management Bureau, Beijing,,School of Land Science and Technology, China University of Geosciences (Beijing),, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于线性光谱混合模型(LSMM, Linear Spectral Mixture Model),引入形态学空间格局分析(MSPA,Morphological Spatial Pattern Analysis)进行城市地域绿色景观连通性评价。根据城市绿色景观特点和MSPA方法中的7种连通性类型的涵义,定义了城市绿色景观连通性功能类型。以深圳市1986年、1995年、2000年、2005年及2010年五期Landsat TM影像为数据源,应用线性光谱混合模型提取植被覆盖率,得到深圳市植被覆盖图。在此基础上,提取出高、全植被覆盖作为目标像元进行MSPA处理,分析植被覆盖状况与绿色景观功能类型的时序总体特征及空间梯度动态。结果表明:深圳市绿色景观破碎程度较高,表现为对结构连通性贡献最小的斑块类型总数最大。城市内部东西部连通性呈现出不同变化的趋势;右侧外圈层的大鹏半岛结构连通性最佳;在同一城市化发展梯度上,东部的样带连通性水平比西部要好。在城市化过程中,深圳市高、全覆被植被像元连通性大小受以下因素的影响:城市化程度,地形因素及区域定位。在同一城市化程度上,地形因素对景观连通性的影响较大。从整体的时间变化和空间梯度动态分析可知,在快速城市化过程中植被覆盖率和连通性功能均下降,而到稳定城市化阶段植被覆盖率和连通性均得到改善。研究表明线性光谱混合模型与形态学空间格局分析相结合可以较好的表征城市绿色景观连通性类型时空分布特征,进而明晰城市化过程与区域内绿色景观数量及连通性动态变化关系。 Abstract:Urban area is the main environment where human are living. The stability of internal ecosystem of a city is highly relevant with its sustainable development. Besides, the connectivity of urban green landscapes is a symbol of the integrity and stability of regional ecological functions. Urbanization has brought a dramatic transformation to urban landscape connectivity. The research on the dynamic changes of landscape connectivity is not only significant to the stability of an urban ecosystem, but also provides a basis for regional biodiversity conservation, urban planning, and land use planning and management. However, current connectivity indicators have obvious limitations, for example, indices over different landscape patterns may show familiar values; graph theory requires a human interpretation because of redundancy data, and research on large-scale landscape may cause data extinction during processing. In this paper, Linear Spectral Mixture Model (LSMM) was integrated into Morphological Spatial Pattern Analysis (MSPA) to evaluate the spatial and temporal dynamics of green landscape connectivity in Shenzhen. According to the urban landscape characteristics and MSPA theory, 7 types of connectivity were defined for urban green landscapes, and then the change features among different urbanization gradients were analyzed. We defined urbanization gradients as three different circle layers based on urbanization density, with a decrease of dense values from the first circle to the third. The main steps followed: 1) LSMM was applied to extract the vegetation coverage information from multi-temporal Landsat TM images. On that, the high and full covered vegetation pixels were defined as the foreground pixels (green landscape) in MSPA approach. 2) 7 types of connectivity were utilized to reveal the temporal and spatial variations of green landscapes in the process of urbanization. The results demonstrated that: 1) over 27% of green landscapes in Shenzhen did not contribute to connectivity during 24a. 2) The transition matrix of connectivity-pattern categories from 1986 to 2010 indicated that the connectivity areas were sharply fluctuated during 24a, and the majority of classes changed into non-green landscapes. Except for the core category, the areas of other connectivity categories showed an upward trend. The connectivity of internal urban landscapes showed different trends between eastern part and western part, and, the Dapeng Peninsula in the third circle showed the best connectivity among the whole city. The peak interval of connected categories showed that the eastern part of Shenzhen had more connectivity providers than the western part. 3) The overall connectivity of Shenzhen's green landscapes followed a change of "decrease-increase" in sequence. Comparing with Shenzhen's urbanization process, it is proved that the quantity and connectivity of green landscapes were affected by the following factors: urbanization level, topographic factor and regional policy. Additionally, it is found that the topographic factor had the greatest influence within the same urbanization level. The results from temporal variations and spatial gradients demonstrated that both vegetation coverage and connectivity showed a downward trend in rapid urbanization process, while the two have been improved in steady urbanization stage. The experimental results prove that the jointly analytical framework is efficiently applied to reveal the spatial and temporal dynamics of connectivity characteristics for urban green landscapes during the process of urbanization. Furthermore it enables us to know the relationship between urbanization and urban green landscape connectivity. This research can be applied in practice and provide benefits for monitoring urban green landscapes. 参考文献 相似文献 引证文献

Modelo de Estabilidade dos Solos a Erosão

The development of new techniques that facilitate environmental planning are needed, so this work presents a new methodological approach : the Model of Stability of Soil Science (MES) , which uses data obtained by remote sensing in order to support environmental planning . The technique was applied in the city of Costa Rica - MS with Landsat generating an environmental zoning proposal for the study area. The letter slope was drawn through the SRTM digital elevation model. The Charter Fitness of Lands (agricultural capability of the soil) was obtained from the Multi Referral Atlas of the State of Mato Grosso do Sul linear spectral mixture model to derive vegetation fraction image ( letter of vegetation cover ) was used . With this data set we applied the MES through map algebra, which uses the overlay of information layers slope, agricultural soil suitability and land cover. Data obtained via MES were compared with those observed in the field. Thus it can be seen that the MES was efficient in assessing the areas most vulnerable to the erosive action, moreover, proved to be an efficient tool for decision making.

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts.

Analyzing the effect of shadow on the relationship between ground cover and vegetation indices by using spectral mixture and radiative transfer models

We present and evaluate an experimental relationship between the fraction of ground cover (FV) and multispectral vegetation indices (VI) derived from medium resolution images (Landsat 5-TM) in vertical shoot trellised vineyards. The results indicate a strong linear relationship between FV and the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), resulting in correlation coefficients greater than 0.90. These relationships were evaluated for the effect of variations in illumination angles and shadow enlargement using two analytical approaches: the Linear Spectral Mixture Analysis techniques and a radiative transfer approach with the Markov-chain canopy reflectance model, with additions to simulate the row structure. Previous to this analysis, both models were evaluated by comparing the model results with VIs in row vineyards obtained from satellite images, performing fairly well. The exploratory analysis demonstrated that the use of a single relationship based on the NDVI index could result in significant inaccuracies for larger zenith angles and row directions perpendicular to the sun azimuth at the satellite acquisition time. In contrast, the SAVI improved the linearity of the relationships and resulted in less sensitivity to changes in the sun angles and row directions.

The influence of the conservation status and changes in the rainfall regime on forest-savanna mosaic dynamics in Northern Brazilian Amazonia

Coupled carbon/climate models are predicting changes in Amazon carbon and water cycles for the near future, with conversion of forest into savanna-like vegetation. However, empirical data to support these models are still scarce for Amazon. Facing this scenario, we investigated whether conservation status and changes in rainfall regime have influenced the forest-savanna mosaic over 20 years, from 1986 to 2006, in a transitional area in Northern Amazonia. By applying a spectral linear mixture model to a Landsat-5-TM time series, we identified protected savanna enclaves within a strictly protected nature reserve (Maracá Ecological Station - MES) and non-protected forest islands at its outskirts and compared their areas among 1986/1994/2006. The protected savanna enclaves decreased 26% in the 20-years period at an average rate of 0.131 ha year-1, with a greater reduction rate observed during times of higher precipitation, whereas the non-protected forest islands remained stable throughout the period of study, balancing the encroachment of forests into the savanna during humid periods and savannization during reduced rainfall periods. Thus, keeping favorable climate conditions, the MES conservation status would continue to favor the forest encroachment upon savanna, while the non-protected outskirt areas would remain resilient to disturbance regimes. However, if the increases in the frequency of dry periods predicted by climate models for this region are confirmed, future changes in extension and directions of forest limits will be affected, disrupting ecological services as carbon storage and the maintenance of local biodiversity.

Quantifying Nonlinear Spectral Mixing in Vegetated Areas: Computer Simulation Model Validation and First Results

Our understanding of nonlinear mixing events in vegetated areas is currently hampered by a pertinent lack of well-validated datasets. Most quantification and modeling efforts are, therefore, based on the theoretical assumptions or indirect empirical observations. Here, we performed a quantitative and qualitative evaluation of the accuracy of nonlinear mixing effects as modeled by a fully calibrated virtual orchard model (based on physically based ray-tracer software). For validation, we had available data from an in situ experiment. This experiment comprised in situ measured mixed pixel reflectance spectra, pixel-specific endmember spectra, and subpixel cover fraction distributions, all collected in the same orchard for which the virtual model was calibrated. We took advantage of this unique-coupled dataset to demonstrate that both the nature and the intensity of the nonlinear mixing events observed in the in situ data are realistically modeled by the ray-tracing software. This is an important observation because this implies that our virtual model now provides a solid tool for the detailed study of nonlinear mixing in vegetated areas which could facilitate as such the design, calibration, and validation of different nonlinear mixing modeling approaches. Initial results revealed that the nonlinear mixing is dependent on fractional distribution, soil moisture conditions, and endmember definitions. We could further demonstrate that the bilinear spectral mixture model nicely described nonlinear mixing events but at the same time overestimated reflectances in spectral regions with moderate-to-low nonlinear mixing behavior.

Construction of lake bathymetry from MODIS satellite data and GIS from 2003 to 2011

In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang (Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was −0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.

Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization

Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectral mixture model. Semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost.

A Fully Constrained Linear Spectral Unmixing Algorithm Based on Distance Geometry

Under the linear spectral mixture model, hyperspectral unmixing can be considered as a convex geometry problem, in which the endmembers are located in the vertices of simplex enclosing the hyperspectral data set and the barycentric coordinates of observation pixels with respect to the simplex correspond to the abundances of endmembers. Based on distance geometry theory, in this paper we propose a new approach for abundance estimation of mixed pixels in hyperspectral images. With the endmember signatures, which is known a priori or can be obtained from the endmember extraction algorithms, the proposed method automatically estimates the abundances of endmembers at each pixel using convex geometry concepts and distance geometry constraints. In the algorithm, denoting the pairwise distances with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of the observation pixels. Another characteristic of this algorithm is that the optimal estimated points of observation pixels as well as the least distortion in geometric structure of original data set can be obtained with the distance geometry constraint. Simultaneously, the use of barycenter of simplex builds an accurate and efficient method to estimate endmembers with zero abundance and, as a result, the subsimplex containing the estimated points is obtained. A comparative study and analysis based on Monte Carlo simulations and real data experiments is conducted among the proposed algorithm and three state-of-the-art algorithms: fully constrained least squares (FCLS), FCLS computed using constrained sparse unmixing by variable splitting and augmented Lagrangian, and simplex-projection unmixing (SPU). The experimental results show that the proposed algorithm always provides the best unmixing accuracy and when the number of endmembers is not very large the algorithm has a lower computational complexity.

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences. The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas. The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods (i.e. change vector analysis and principal component analysis-based method). In particular, the proposed sub-pixel change detection approach not only provides the binary change information, but also obtains the characterization about change direction and intensity, which greatly extends the semantic meaning of the detected change targets.