An enhanced wavelet-based compression scheme for satellite image is proposed in this paper. The Consultative
Committee for Space Data System (CCSDS) presented a recommendation which utilizes the wavelet transform and the
bit plane coder for satellite image compression. The bit plane coder used in the CCSDS recommendation encodes the
coefficient block of bit planes one by one and then truncates the unnecessary bit plane coefficient blocks. By this way,
the contexts of bit planes are not considered as the redundancy embedded data which may be compressed further. The
proposed scheme uses a bit plane extractor to parse the differences of the original image data and its wavelet transformed
coefficients. The output of bit plane extractor will be encoded by a run-length coder and will be sent to the
communication channel with the CCSDS compressed data. Comparing with the recommendation of CCSDS, under a
reasonable complexity, the subjective quality of the image will maintained or even better. In addition, the bit-rate can be
further decreased from 85% to 95% of the CCSDS image compression recommendation at the similar objective quality
level. By using the lower bit rate lossy mode compression and bit plane compensation, it is possible to obtain lower bit
rate and higher quality image than which the higher bit rate lossy mode compression can achieve.
In this paper, a near lossless medical image compression scheme combining JPEG-LS with cubic spline interpolation (CSI) is presented. The CSI is developed to subsample image data with minimal distortion and to achieve image compression. It has been shown in literatures that the CSI can be combined with the transform-based image compression algorithm to develop a modified image compression codec, which obtains a higher compression ratio and a better subjective quality of reconstructed image than the standard transform-based codecs. This paper combines the CSI with lossless JPEG-LS to form the modified JPEG-LS scheme and further makes use of this modified codec to medical image compression. By comparing with the JPEG-LS image compression standard, experimental results show that the
compression ratio increased over 3 times for the proposed scheme with similar visual quality. The proposed scheme reduces the loading for storing and transmission of image, therefore it is suitable for low bit-rate telemedicine application. The modified JPEG-LS can reduce the loading of storing and transmitting of medical image.
In this paper, a modified image compression algorithm using cubic spline interpolation (CSI) and bit-plane compensation
is presented for low bit-rate transmission. The CSI is developed in order to subsample image data with minimal
distortion and to achieve image compression. It has been shown in literatures that the CSI can be combined with the
JPEG or JPEG2000 algorithm to develop a modified JPEG or JPEG2000 CODEC, which obtains a higher compression
ratio and better quality of reconstructed images than the standard JPEG and JPEG2000 CODECs in low bit-rate range.
This paper implements the modified JPEG algorithm, applies bit-plane compensation and tests a few images.
Experimental results show that the proposed scheme can increase 25~30% compression ratio of original JPEG data
compression system with similar visual quality in low bit-rate range. This system can reduce the loading of
telecommunication networks and is quite suitable for low bit-rate transmission.
KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Detection and tracking algorithms, Spatial filters, Wavelets, Remote sensing, Interference (communication), Linear filtering, Computer simulations, Data processing
Target detection algorithms for hyperspectral remote sensing have been studied for decades. The Least Square (LS) approach is one of the most widely used algorithms. It has been proved that the Noise Whitened Least Square (NWLS) can outperform the original version. But in order to have good results, the estimation of the noise covariance matrix is very important and still remains a great challenge. Many estimation methods have been proposed in the past, including spatial and frequency domain high-pass filter, neighborhood pixel subtraction, etc. In this paper, we further adopt the Fully Constrained Least Square (FCLS), which combine sum-to-one and non-negative constraints, with the NWLS and we also conduct a quantitative comparison with computer simulation of material spectrum from AVIRIS data base on the detection performance and the difference from the designed noise covariance matrix. We will also compare the results with real AVIRIS image scene.
Anomaly detection for remote sensing has drawn a lot of attention lately. An anomaly has distinct spectral features from its neighborhood, whose spectral signature is not known <i>a priori</i>, and it usually has small size with only a few pixels. It is difficult to detect anomalies, and it is more challenge to detect anomalies without any information of the background environment in hyperspectral data with hundreds of co-registered image bands. Several methods are devoted to this problem, such as the well-known RX algorithm which takes advantage of the second-order statistics. The RX algorithm assumes Gaussian noise and uses sample covariance matrix for data whitening. However, when the anomalies pixel number exceeds certain percentage or the data is ill distributed, the sample covariance matrix can not represent the background distribution. In this case, the RX algorithm will not perform well. In order to solve this problem, in this paper we propose a weighted covariance matrix for anomaly detection. It gives weight to the each pixel in the covariance matrix by its distance to the data center, and then followed by the anomaly detection approach based on second-order statistics. We will compare the experimental results with the original RX methods.