Hairong Tang

Preliminary evaluation of the long-term GLASS albedo product

Land surface albedo is an important parameter to describe the radiant forcing in the climate system. A long-time series of global albedo products is needed to understand the mechanism of climate change. Aiming to support global change and Earth system studies, GLASS (Global LAnd Surface Satellites) provides long-term global land surface albedo product from 1981 to 2010, which are generated from multisource remote sensing data and newly developed algorithms. It is critical to assess the quality of the GLASS product when it is released to the public. This paper first introduced the algorithms and then analyzed the integrity, accuracy, and robustness of the GLASS albedo product. The results show that the GLASS albedo product is a gapless, long-term continuous, and self-consistent data-set with an accuracy similar to that of the widely acknowledged MODIS MCD43 product. The quality flag, which is provided along with the black-sky and white-sky albedo, gives a pertinent indication of the expected uncertainty in the product.

Optimizing the Endmembers Using Volume Invariant Constrained Model

The linear mixture model (LMM) plays a crucial role in the spectral unmixing of hyperspectral data. Under the assumption of LMM, the solution with the minimum reconstruction error is considered to be the ideal endmember. However, for practical hyperspectral data sets, endmembers that enclose all the pixels are physically meaningless due to the effect of noise. Therefore, in many cases, it is not sufficient to consider only the reconstruction error, some constraints (for instance, volume constraint) need to be added to the endmembers. The two terms can be considered as serving two forces: minimizing the reconstruction error forces the endmembers to move outward and thus enlarges the volume of the simplex while the endmember constraint acts in the opposite direction by driving the endmembers to move inward so as to constrain the volume to be smaller. Many existing methods obtain their solution just by balancing the two contradictory forces. The solution acquired in this way can not only minimize the reconstruction error but also be physically meaningful. Interestingly, we find, in this paper, that the two forces are not completely contradictory with each other, and the reconstruction error can be further reduced without changing the volume of the simplex. And more interestingly, our method can further optimize the solution provided by all the endmember extraction methods (both endmember selection methods and endmember generation methods). After optimization, the final endmembers outperform the initial solution in terms of reconstruction error as well as accuracy. The experiments on simulated and real hyperspectral data verify the validation of our method.

Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection

Few band selection methods are specially designed for small target detection. It is well known that the information of small targets is most likely contained in non-Gaussian bands, where small targets are more easily separated from the background. On the other hand, correlation of band set also plays an important role in the small target detection. When the selected bands are highly correlated, it will be unbeneficial for the subsequent detection. However, the existing non-Gaussianity-based band selection methods have not taken the correlation of bands into account, which generally result in high correlation of obtained bands. In this paper, combining the third-order (third-order tensor) and second-order (correlation) statistics of bands, we define a new concept, named joint skewness, for multivariate data. Moreover, we also propose an easy-to-implement approach to estimate this index based on high-order singular value decomposition (HOSVD). Based on the definition of joint skewness, we present an unsupervised band selection for small target detection for hyperspectral data, named joint skewness band selection (JSBS). The evaluation results demonstrate that the bands selected by JSBS are very effective in terms of small target detection.

A robust and efficient band selection method using graph representation for hyperspectral imagery

ABSTRACT In the field of unsupervised band selection, both robustness and efficiency are of great importance. In this article, we propose a new unsupervised band selection method termed graph representation based band selection (GRBS), which is expected to be insensitive to noisy bands and computationally inexpensive. In GRBS, bands are treated as the nodes of graph in high-dimensional space and centres of the band clusters are considered as the ideal choice. Interestingly, different from other clustering-based band selection methods, GRBS does not involve band clustering. Instead, it employs an easily computed criterion function to select the desired bands, which greatly improves the efficiency. The experiments demonstrate that GRBS has a promising performance and outperforms the compared methods in terms of both accuracy and efficiency.

An efficient band selection method for hyperspectral imageries based on covariance matrix

Band selection plays an important role in reducing the dimensionality of hyperspectral data sets. It is a combinatorial optimization problem for optimal band (feature) subset selection which generally involves high computational complexity. In this paper, we present an efficient band selection methods based on the covariance matrix. The method tries to compute the subset of bands with the largest determinant of covariance matrix. By using a recursive role found in this study, the proposed method can be very efficient. Besides, it also has the potentiality to determine the number of required bands.

Investigation on the dynamics of artificial surface reflectance under field condition

Reflectance of typical artificial surfaces, such as road and roof is usually believed to be invariant in a short period of time due to their physical and chemical stability. To examine its variability, reflectance of asphalt, concrete, walkway slab, asphalt roof paper and color plate is measured using dual-beam method. A preprocessing procedure is performed to remove possible errors caused by non-target factors. Measurement results show that reflectance of these targets are relatively stable when sun elevation condition is large and change rapidly when sun elevation is close to 0 °. This result provides useful information about reflectance variation of typical urban surfaces under changing field condition and may benefit reflectance modeling studies on similar targets.