Linear Spectral Unmixing Method Research Articles

Applications of Kalman Filtering to Single Hyperspectral Signature Analysis

Kalman filter (KF) is a widely used statistical signal processing technique for parameter estimation. Recently, a KF-based approach to linear spectral unmixing, called KF-based linear spectral unmixing (KFLU) was developed for mixed pixel classification. However, its applicability to spectral characterization for spectral estimation, identification, and quantification has not been explored. This paper presents new applications of Kalman filtering in spectral estimation, identification and abundance quantification for which three KF-based spectral characterization signal processing techniques are developed. These techniques are completely different from the KFLU in the sense that the former performs a KF across a spectral coverage wavelength by wavelength as opposed to the latter, which implements a Kalman filter pixel vector by pixel vector throughout an entire image cube. In addition, the proposed KF-based techniques do not require a linear mixture model as KFLU does. Accordingly, they are not linear spectral unmixing methods, but rather spectral signature filters operating as if they are spectral measures.

Automatic cloud removal from multi-temporal SPOT images

Partial cloud cover is a severe problem in the optical remote sensing images. The problem can be mostly overcome by mosaicking the cloud-free areas of the multi-temporal images. In this paper, multidisciplinary methods are proposed to generate cloud-free mosaic images from multi-temporal SPOT images in three steps. At first, the original images are enhanced in both brightness and chromaticity. Secondly, the linear spectral unmixing method (LSU) is used to extract all cloud cover regions. Then, we choose the base image that has the least thin-cloud cover and divide the base image into grid zones. We find the thin-cloud and cloud-shadow zones in the eight neighbors of the thick-cloud zones based on the relative locations and the sun elevation angle. At last, the cloud and cloud-shadow zones of the base image are replaced by the same-location cloud-free zones on other images. Between each two zones of the base image and the replacing image, we create a transition zone. The multiscale wavelet-based fusion method is then used to fuse the pixels in the zones to generate cloud-free satellite images. Based on our complete and sophisticated approach, the high-quality fused results are produced from the source images that have variant brightness.

Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices

It is important to estimate vegetation fraction for forecasting regional weather and in precision agriculture for assessing crop performance during emergence and early growth phases. In this study, two approaches, linear spectral unmixing and vegetation indices, were reviewed and evaluated for the estimation of crop fraction from hyperspectral data. Compact Airborne Spectrographic Imager (casi) hyperspectral data were acquired three times in the 2001 growing season over four agricultural fields to monitor crop growth conditions and develop procedures for delineating major subunits for crop management. Crops planted in these fields included corn, soybean, and wheat. End-member spectra were extracted from casi data and used for linear spectral unmixing. Various vegetation indices, including the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), modified soil-adjusted vegetation index (MSAVI), transformed soil-adjusted vegetation index (TSAVI), and recently developed modified triangular vegetation index (MTVI2) and VI700 and VIgreen indices, were evaluated with casi data and with simulated spectra using coupled PROSPECT and SAILH models. All these indices were highly correlated with measured crop fractions. A comparison study based on simulated spectra showed that MTVI2 maintained adequate sensitivity up to a higher crop coverage. A high coefficient of determination (R2 = 0.90) and a low root mean square error (RMSE = 0.10) were obtained between measured and estimated crop fraction using MTVI2. The crop fraction derived from linear spectral unmixing was also highly correlated with the measured crop fraction (R2 = 0.94; RMSE = 0.08). However, determining end-member spectra in the linear spectral unmixing method remains a challenge. Using vegetation indices is a convenient method for crop fraction estimation with satisfactory accuracy.

Scaling dimensions in spectroscopy of soil and vegetation

The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration.Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce (Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed.We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities.Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping.All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.

ANALYSIS OF LAND SAT-5 TM IMAGERY FOR EXTRACTING AQUACULTURE FARMS INFORMATION IN THE KOREAN COASTAL WATERS

The objective of the present study is to compare certain conventional satellite image processing techniques with the recently evolved linear spectral unmixing method and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal region of Korea. Various conventional techniques existed to extract such information are spectral enhancement and classification. However, these techniques performed on the Landsat-TM imagery do not seem to yield accurate information about the aquaculture farms, and instead lead to misinterpretation and misclassification. A large discrepancy between the actual and extracted information results from spectral confusion and inadequate spatial resolution of the sensor, which leads to occurrence of mixture pixels or mixels, which are known to be the source of errors in the classified image. To over come this problem, more recently evolved methods such as step-by-step iterative partial spectral end-member extraction linear spectral unmixing methods are attempted. Large errors in extraction of aquaculture farms information through the conventional classification techniques are significantly minimized with the step-by-step iterative partial spectral end-member extraction approach and the accuracy of classification is further improved with linear spectral unmixing approach. The aquaculture fraction derived from unmxing of TM image data was validated using NDVI values in absence of field data. NDVI and aquaculture fraction are positively correlated (R2 = 0.91), indicating the reliability of the sub-pixel classification

Application of Imaging Spectrometer Data in Identifying Environmental Pollution Caused by Mining at Rodaquilar, Spain

The Rodaquilar mining area in southern Spain has been mined for gold for an extensive period, most recently in the 1940s and 1950s. This activity has resulted in waste rock and tailings being dispersed from the mine workings down to a large tailings dump and then along a river valley eventually reaching the sea. This tailings dump material consists of a variety of ferruginous materials that often contain trace elements that are environmentally harmful and possibly toxic. These ferruginous materials have distinctive spectral features which make them amenable to detection and mapping by airborne imaging spectrometer data. The dispersion of the tailings material was studied using two semiquantitative techniques, matched filtering and linear spectral unmixing and qualitative methods using band ratios and the variation in strength of spectral features. The distinct ferruginous mineral phases resolved by these methods were then compared to laboratory reference spectrum using qualitative analysis of the spectral profiles and two quantitative techniques, spectral angle mapping and cross correlogram spectral matching. The distribution of the ferruginous materials identified from the imaging spectrometer data supports the results of laboratory experiments on the dependence of the formation of iron species on their geochemical and physical situation.