The commercialization of high resolution remote sensing image provides the image application more widely space. How
to extract the interested important objects quickly and exactly from remote sensing image is always the research focus.
After analyzed the characteristics of road in high resolution image, the paper constructed the road extraction model based
on MRF and Bayesian. And finally the validity of the method was confirmed by an example.
Compression techniques are required to transmit the large amounts of high-resolution synthetic aperture radar (SAR) image data over the available channels. Common Image compression methods may lose detail and weak information in original images, especially at smoothness areas and edges with low contrast. This is known as "smoothing effect". It becomes difficult to extract and recognize some useful image features such as points and lines. We propose a new SAR image compression algorithm that can reduce the "smoothing effect" based on adaptive wavelet packet transform and feature-preserving rate allocation. For the reason that images should be modeled as non-stationary information resources, a SAR image is partitioned to overlapped blocks. Each overlapped block is then transformed by adaptive wavelet packet according to statistical features of different blocks. In quantifying and entropy coding of wavelet coefficients, we integrate feature-preserving technique. Experiments show that quality of our algorithm up to 16:1 compression ratio is improved significantly, and more weak information is reserved.
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.
This paper addresses the problem of image compression in remote sensing applications. Compared with other still images, remote-sensing images are characterized with complex textures and weak local correlation. By using wavelet transform, the coefficients have showed a spatial clustering trend in wavelet domain. Most of current algorithms of image compression have not taken this clustering into account. In order to further improve coding efficiency, an efficient remote sensing image coding algorithm based on morphological wavelet is proposed. First, the fast multi-scale wavelet transform is applied to image; second, a morphological operator is designed to capture the clusters and fully exploit the redundancy between the coefficients. Compression is then achieved by using this non-linear method. For multi-bands remote-sensing images, a Prior Important Band (PIB) method is used to decorrelate the correlations in the spectral dimension, the above coding algorithm is then applied to the bands. In the experiment, the author selects one AVARIS hyper-spectral image and two satellite images to test the performance of the algorithm. Experimental results illustrate that it provides higher performance than JPEG2000 in low-bits compression and it is suitable to multi-band images too.
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