As for the shortcoming that traditional interpolation methods often cause the over-smooth problem, or couldn't fully take
the variety of the terrain detail into account, this paper proposed a DEM densification method by using shape from
shading (SFS) based on spectral information from single highly spatial resolution satellite image. In accordance with the
idea of introducing SFS into DEM interpolation, a method is put forward, which is under the condition of the unknown
light source in spatially heterogeneous area. Surface relative shape was reconstructed at first, and the second order edgeoriented
image interpolation method was applied to generate a high-resolution DEM grid. Spectral information of the
unknown points was used to reveal the actual surface reflection properties, and land surface could be accurately modeled
compared traditional SFS method, which use a constant reflectance in the whole region. Experiments proved that the
algorithm is very effective for the sparse grid DEM interpolation and offer a new way for DEM densification.
The satellite imaging system is affected by optical diffraction limitation, atmosphere disturbance, CCD under-sampling
noise and etc. Then the information beyond image system cut-off frequency is lost, images degrade and spatial resolution
decrease. Structure information becomes undistinguishable, which is fatal to manual interpretation and adaptive target
recognition. In this paper, one structure information preserved scheme is proposed. Taking into account the anisotropic
diffuse property of PSF (point spread function) of in-track and cross-track direction, the sparse property of nature image
and noise level, with data-driven kernel function sub-pixel estimation, the method restore high spatial resolution image
from low one. Joint frequency domain and wavelet domain L1 normal regularization suppress wrinkle and noise
amplified for this ill-posed inverse problem. With CBERS-2 images, this method is proved to improve spatial resolution
and preserve edge and structure effectively without obverse wrinkle. With MTF curve, the spatial resolution is improved
obviously with high PSNR, and the edge is preserved perfectly.
The traditional image quality assessment methods based on pixels have many limitations. Such as the lack of
consideration of the image structure, or the need of a complete reference image. To avoid these problems, this paper
presented a new image quality assessment method based on weighted singular value decomposition in wavelet domain
(WWSVD). In this algorithm, the singular value vector difference and the mean bias between the original image and the
distorted image are considered to evaluate the distortion degree. Many tests were conducted to evaluate the performance,
the 227 testing images of JPEG2000 compression were come from the Live Image Quality Assessment Database,
Release 2005. The results showed a great improvement in both the consistency with the DMOS (Differential Mean
Opinion Score, DMOS) and the stability when applied to a large range of compression rates.
A modified fire spread fast model combining CA framework with WangZhengfei's model is proposed for Emergency
Rescue system.This model combines weather condition, terrain slope and vegetation type. It is suit to intricate
topography and environmental southwestern fire-prone areas in China. A grid and vector polygon including the
outermost fire fronts is obtained for GIS spatial inquiring, providing support for Aid in Decision Making. Simulation and
experiments prove cellular automata feasible and effective for fast fire spread model, special in multi-factors restrained
forest fire simulation.
The stereo image pair taken from different positions, theoretically the redundant information and disparity allows to
reconstruction higher resolution images and reduces the aliasing artifacts. We present an improved hybrid MAP
(Maximum a Posteriori) restoration algorithm for stereo image pair combined PSF (Point Spread Function) measurement
and iterative registration parameters updated scheme. With the regularity parameter choice based on L-curve, this
method preserves discontinuity efficiently. The reconstruction images quality lies on the accurate registration, the PSF
non-bias estimation and regulation parameters choice. In this paper, an MTF (Modulation Transfer Function) measure
and spatial profile methods are presented for reconstruction images quality estimation. A simulation and a real-data
experiment demonstrate the performance of the algorithm.
KEYWORDS: Sensors, Sensor networks, Wavelets, Head, Stochastic processes, Environmental sensing, Data communications, Data processing, Signal processing, Geographic information systems
Data aggregation is a fatal for wireless routing in sensor networks, which combine data coming from different sources and routes, eliminates redundancy, minimizes the number of transmissions, and saves energy. We propose an in-cluster CISP (Collaborative Information and Signal Processing) method aim at dealing with spatiotemporal redundancy issue of irregular sample. This tradeoff of computation and communication energy consume for sensor network. As for stochastic deployment and dynamic topology of WSN, a distributed algorithm of Lift Scheme for de-correlation and multi-scale data aggregation approach is put forward. Then one middleware is implemented basing on it, which is proved valid with experiment for redundancy information reduction. This ubiquitous local algorithm not only decrease sharply the communication cost when transmitting information to cluster head with approximate information reserved, but also deals with the fundamental issue of spatiotemporal irregular samples.
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