Search Results (7)

Remote sensing image registration has been widely applied in military and civilian fields, such as target recognition, visual navigation and change detection. The dynamic changes in the sensing environment and sensors bring differences to feature point detection in amount and quality, which is still a common and intractable challenge for feature-based registration approaches. With such multiple perturbations, the extracted feature points representing the same physical location in space may have different location accuracy. Most existing matching methods focus on recovering the optimal feature correspondences while they ignore the diversities of different points in position, which easily brings the model into a bad local extrema, especially when existing with the outliers and noises. In this paper, we present a novel accuracy-aware registration model for remote sensing. A soft weighting is designed for each sample to preferentially select more reliable sample points. To better estimate the transformation between input images, an optimal sparse approximation is applied to approach the transformation by multiple iterations, which effectively reduces the computation complexity and also improves the accuracy of approximation. Experimental results show that the proposed method outperforms the state-of-the-art approaches in both matching accuracy and correct matches. Full article

Geostationary (GEO) satellite sensors provide earth observation data with a high temporal frequency and can complement low earth orbit (LEO) sensors in monitoring terrestrial vegetation. Consistency between GEO and LEO observation data is thus critical to the synergistic use of the sensors; however, mismatch between the sun–target–sensor viewing geometries in the middle-to-high latitude region and the sensor-specific spectral response functions (SRFs) introduce systematic errors into GEO–LEO products such as the Normalized Difference Vegetation Index (NDVI). If one can find a parameter in which the value is less influenced by geometric conditions and SRFs, it would be invaluable for the synergistic use of the multiple sensors. This study attempts to develop an algorithm to obtain such parameters (NDVI-based indices), which are equivalent to fraction of vegetation cover (FVC) computed from NDVI and endmember spectra. The algorithm was based on a linear mixture model (LMM) with automated computation of the parameters, i.e., endmember spectra. The algorithm was evaluated through inter-comparison between NDVI-based indices using off-nadir GEO observation data from the Himawari 8 Advanced Himawari Imager (AHI) and near-nadir LEO observation data from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference over land surfaces in Japan at middle latitudes. Results showed that scene-dependent biases between the NDVI-based indices of sensors were $-0.0004\pm 0.018$ (mean ± standard deviation). Small biases were observed in areas in which the fractional abundances of vegetation were likely less sensitive to the view zenith angle. Agreement between the NDVI-based indices of the sensors was, in general, better than the agreement between the NDVI values. Importantly, the developed algorithm does not require regression analysis for reducing biases between the indices. The algorithm should assist in the development of algorithms for performing inter-sensor translations of vegetation indices using the NDVI-based index as a parameter. Full article

Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM). HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM) in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP) architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations. Full article

The spectral unmixing of a linear mixture model (LMM) with Normalized Difference Vegetation Index (NDVI) constraints was performed to estimate the fraction of vegetation cover (FVC) over the earth’s surface in an effort to facilitate long-term surface vegetation monitoring using a set of environmental satellites. Although the integrated use of multiple sensors improves the spatial and temporal quality of the data sets, area-averaged FVC values obtained using an LMM-based algorithm suffer from systematic biases caused by differences in the spatial resolutions of the sensors, known as scaling effects. The objective of this study is to investigate the scaling effects in area-averaged FVC values using analytical approaches by focusing on the monotonic behavior of the scaling effects as a function of the spatial resolution. The analysis was conducted based on a resolution transformation model introduced recently by the authors in the accompanying paper (Obata et al., 2012). The maximum value of the scaling effects present in FVC values was derived analytically and validated numerically. A series of derivations identified the error bounds (inherent uncertainties) of the averaged FVC values caused by the scaling effect. The results indicate a fundamental difference between the NDVI and the retrieved FVC from NDVI, which should be noted for accuracy improvement of long-term observation datasets. Full article

The fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM) to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors in the measured reflectance spectra. The objective of this study was to derive a factor that could be used to assess the robustness of LMM-based algorithms under a two-endmember assumption. The factor was derived from the analytical relationship between FVC values determined according to several previously described algorithms. The factor depended on the target spectra, endmember spectra, and choice of the spectral vegetation index. Numerical simulations were conducted to demonstrate the dependence and usefulness of the technique in terms of robustness against the measurement noise. Full article

Remotely sensed reflectance spectra may be biased by several intervening factors, and the biases are propagated into estimations of the fraction of vegetation cover (FVC) by algorithms based on a linear mixture model (LMM). The errors propagated in FVCs depend on the retrieval algorithm used, due to differences in the assumptions of the model as well as constraints employed in the algorithm. These differences should be fully understood prior to algorithm selection for practical applications. Although numerous studies have investigated the relationships between errors propagated by different algorithms, these relationships have not been fully understood from a deterministic perspective. This study introduces a technique for deriving the analytical underpinnings of error propagation in FVC based on several LMM-based algorithms. The derivation assumes that measurement noise is band-correlated additive noise. The bias errors propagated in FVC depended on the endmember spectra assumed in the algorithm, the target spectrum, and the coefficients of the spectral vegetation index, which were employed as constraints, as well as magnitude of the input error. It was found that the relationships among the propagated errors assume asymmetric elliptical forms with coefficients that are determined by the input variables. These results suggest that the relationships depend heavily on the choice of endmember spectra as well as the spectrum of the target pixel and the vegetation index employed as a constraint. The present findings should assist in the selection of an optimum algorithm based on prior knowledge of the target field. Full article

Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved FVCs from a single spectrum depend on those choices. Therefore, the mechanism underlying this dependency must be understood fully to improve the interpretation of the results. The objective of this study is to clarify the relationships among algorithms based on the LMM. The relationships were derived analytically by limiting both the number of endmembers and the spectral wavelength band to two each. Numerical experiments were conducted to demonstrate and validate the derived relationships. It was found that the relationships between two algorithms of this kind could be characterized by a single parameter that was determined by the endmember spectra and the coefficients of a VI model equation used in the algorithms. Full article