Lambertian surfaces represent an important assumption when constructing thermal radiance transfer equations for remote sensing observations of ground surface temperatures. We identify the properties of ground surfaces in thermal infrared regions as Lambertian surfaces via field experiments. Because Lambertian surfaces present homogeneous thermal emissions levels in hemispheric directions for a specific ground surface under specific kinetic temperatures and emissions, we conducted a series of field experiments to illustrate the properties of such ground surfaces. Four typical ground surfaces were selected for the experiments to observe thermal emissions: bare soil, grass, water, and concrete. Radiance thermometers were used to observe ground emissions from seven directions: 30°, 45°, 60°, 90°, 120°, 135°, and 150°. Solar zenith angles were considered for the observation of ground emissions. Experiments were conducted in five different regions of China (Beijing, Nanjing, Xilinguole, Yongzhou, and Jiangmen) during both daytime and nighttime. To determine whether different observation angles have significantly different effects on radiance, statistical analyses (ANOVA and Friedman test) were conducted. Post hoc multiple comparison tests and pairwise multiple comparisons were also conducted to examine the various pairings of observation angles and to measure the radiance differences. Roughly half of the radiance groups of all observed sites were tested via an ANOVA, and the remaining groups with unequal variances were subjected to the Friedman test. The results indicate that statistically significant differences in the radiance levels occurred among the seven angles for almost all of the sites (39 of the 40 groups). The results of our experiments indicate that the selected ground surfaces, especially the grass and the bare soil, may not behave with Lambertian properties in the thermal infrared region. This is probably attributed to the roughness of the selected surface, because we found that roughness is an important factor affecting the observed magnitude of thermal emission from different directions of the ground surface under study. Therefore, whether or not a terrestrial surface can be assumed to be a Lambertian surface should be based on their geometric structure. When the surface is relatively smooth, we can say that it is close to the Lambertian property in thermal emission.

Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree (DRND-DD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration (LNC) for hyperspectral data by using hybrid gene expression programming (REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis (PCA), singular value decomposition (SVD), a dimensionality reduction algorithm based on the positive region (AR-PR) and a dimensionality reduction algorithm based on a discernable matrix (AR-DM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

This paper presents gene expression programming for attribution reduction in rough set (GEP-ARRS), which designs a new GEP code to convert attribution reduction into an expression tree and a new fitness function. Meanwhile, to solve optimal reduction, GEP ARRS implements a dynamic population creation strategy to reduce the gene length of GEP to accelerate solution efficiency of GEP. Through extensive experiments on mass or high-dimensional data sets, it is shown that GEP-ARRS is apparently more advantageous in terms of speed and quality in contrast with traditional attribution reduction algorithms on intelligence computing.