SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.
As one of the most important aquatic products raising bases of Guangdong province in China, Zhelin Bay has
experienced high intensity of exploitation and utilization during the recent decades. This paper aims at the dynamic
change of Zhelin Bay, multi-source data of digital land use map, topographical map, and geomorphological map of the
National Coastal Survey of china in 1980s, Landsat TM satellite imagery obtained in 2000, land use data in 2000, as well
as SPOT imagery and land use data from the newly National 908 Remote Sensing Survey were used. The data were
preprocessed in a uniform mathematical foundation at first. Water area rate, open degree, and morphology coefficient
which can depict the change of bays in different respect were taken as quantitative indicators to analyze the
morphological changes of Zhelin Bay. The classification based on these indicators was then made in each period of time.
Finally, the comprehensive spatio-temporal change of the bay was evaluated in a Changing Index model. Analysis results
show that, during the latest 20 years, the water area rate has changed evidently from 0.8503 to 0.7410, leading to the
category of Zhelin Bay changed from entire-water bay to much-water bay. Besides, the Changing Index of Zhelin Bay
during the latest 20 years is 0.44%. Reasons for the change were discussed and some suggestions were given in the end
of the paper.
Beijing-1 small satellite image of 4m high resolution not only makes it possible to extract the detailed information that is
difficult to be obtained from low-resolution images, but also brings out new research subjects for automatic extraction
and identification of thematic information. The reason for this are as follows:(1) the obvious increase of data requires
higher spatial and temporal efficiency of image data retrieval, display, processing, etc.; (2) due to the highly detailed
information of high resolution image, under the influence of the Bidirectional Reflectance Distribution Function
(BRDF), different parts of the same object may have different grey levels; together with factors such as object shadow,
mutual cover, and cloud cover, the phenomenon of same object, different spectrum of high resolution images becomes
more prominent, and the different object, same spectrum still exists, which brings greater difficulties to the work of
information extraction [1][2]. The road of high resolution image has the following features: (1) the width of the road
varies slightly and slowly; (2) the direction of the road varies slowly; (3) the local mean grey level of the road varies
slowly; (4) the road is relatively long. Due to the increase of the resolution, the noises such as bridges, cars and trees
along the road and its edge also increase. The paper proposes a new road extraction algorithm namely Scansnake aimed
at the features of Beijing-1 images. A large amount of experiments proved that Scansnake algorithm has the advantage of
object tracking, and under a series of complex conditions such as the variation of the width of the road and the change of
grey feature distribution, Scansnake method can extract the road information of the high resolution Beijing-1 image
Beijing-1 small satellite carries a 4m panchromatic sensor and a 32m multi-spectral sensor, with both the features of the
high resolution of SPOT and the multi-spectrum of Landsat. However, its resolution and the wavelength range of the
corresponding band are significantly different from the existing satellites. Meanwhile, the difference of the wavelength
ranges between the panchromatic image and the multi-spectral image is rather large, especially in the NIR band where
the difference reaches 80%. Thus, low correlation means that the traditional image fusion methods are not ideal to date.
Aimed at the spectral features of Beijing-1 small satellite high-resolution imagery and multi-spectral imagery, the paper
proposes an image fusion method based on Imagesharp. First, linear functions are used to fit the low-resolution image
into the high-resolution image, and then the fitted high-resolution multi-spectral image and the high-resolution
panchromatic image are fused, which can improve the spatial resolution and keep the original multi-spectral information
well at the same time. At last, in comparison to the traditional HIS and Brovey fusion method, Imagesharp method can
maintain the color saturation and the spectral information better. A large amount of experiments prove that Imagesharp
algorithm is suitable for the Beijing-1 image fusion and it is already specially applied in the Beijing-1 small satellite data
deep processing software.
Beijing-1 small satellite was launched Oct.27 2005 and has taken part in the plan of China high-performance earth observation after finishing on-orbit test period. Two kinds of sensors were carried on the satellite. One is 3-band multi-spectral senor whose spatial resolution was 32m, the other panchromatic sensor whose spatial resolution was 4m. In order to ensure truly utility for small satellite data, preliminary deep processing system had been developed for receiving, preprocessing, and data-distribution. Meanwhile, several key questions must be deal with including radiometric calibration, geometric precise rectification, orthographic rectification, image fusion and application demonstration. The paper will focus on the works of the second part including RPC orthographic rectification model and how to optimize algorithms of orthographic rectification which consider the feature of 4m high spatial resolution. RFM is a generalized sensor model, which uses RPC parameters to perform orthographic rectification in no need of orbit parameters and sensor imaging parameters. It is independent on sensors or platforms and supports any object space coordinate system with a variable coordinate system. Compared to linear transformation and polynomial transform, RFM has the highest positioning accuracy. Because RPC is determined by applying the least squares principle to GCP data, approximate error can be evenly distributed through RFM rectification. Based on the experiment on the Beijing-1 high resolution small satellite data using RFM and improved RFM, a generalized model of orthographic rectification of high resolution small satellite data can be developed. The experiment proves: Using second-order improved RFM to rectify the Beijing-1 small satellite image has a sub-pixel positioning accuracy that is close to the accuracy of the rigorous sensor model based on the collinearity equation when the GCPs are evenly distributed.
Conference Committee Involvement (1)
International Conference on Earth Observation Data Processing and Analysis
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