K. Venugopalan

Speckle reduction in remote sensing images

Speckle noise contaminates image content and thus detracts from image interpretation. Speckle noise is the grainy salt-and-pepper pattern present in radar imagery caused by the interaction of out-of-phase waves with a target. Speckle noise reduction is usually employed prior to further image analysis. The primary goal of speckle filtering is to reduce speckle noise without sacrificing information content. The ideal speckle filter should adaptively smooth speckle noise, retain edges and features, and also preserve subtle but distinguishable details, such as thin linear features and point targets. Various speckle filters have been devised due to their different purposes and different capacities. In the present paper we are analyzing median, Lee and Wiener2 filters for the sampled SAR images from ENVISAT, RADARSAT2, US Library Congress and comparing their performances statistically by analyzing quality parameters like data mean, standard deviation, kurtosis and skewness. The paper is concluded with visual quality comparison of the input images and de-speckled images.

SAR Image Compression Using Wavelet Packets

A SAR (Synthetic Aperture Radar) system usually collects huge amount of data, and focusing of the raw data acquires complex range varying phase compensation techniques, which are generally performed off-board. The large amounts of data generated have to be stored on-board or be transmitted to a ground station via a dedicated data link. Therefore, some form of compression on the raw data provides an attractive option for SAR systems. In this paper we investigate the usage of WPT(Wavelet Packet Transform), which performs uniform division of frequency spectrum, independently on real and imaginary parts of the complex SAR raw data along with scalar quantization for compressing SAR raw data. We propose another image coding algorithm which uses rate distortion optimized WPT.

Amplitude Phase Algorithm for SAR Signal Processing

In space borne SAR systems some form of data compression is required to reduce the bandwidth of the downlink channel. In the present paper we have represented the complex SAR raw data with amplitude-phase (AP) and then applied the devised algorithm. It is observed that the phase information of the compressed data is preserved to the great extent. The quality of the reconstructed data is compared in terms of the important performance evaluation parameters like signal to noise ratio (SNR), standard deviation of the phase (PSD), mean phase error (MPE) and the compression ratio (CR). The amplitude-phase algorithm is compared with that of Block Adaptive Quantization (BAQ) algorithm. The evaluation procedure is carried out in two domains, raw data domain and image domain. Numerical experiments were carried out using ERS-2 satellite data supplied by European Space Agency (ESA) showing that amplitude-phase algorithm provides us with more Compression Ratio (CR) choices than BAQ and for certain CR, AP algorithm provides at least one choice whose performance is better than or equal to that of BAQ. These two algorithms neither affect spatial resolution nor generate geometric distortion. Both of them have only a little effect on radiometric resolution.

Applications of Microwave Sensors in Medicines

Microwave sensors for Biomedical applications are highlighted in this paper. The emphasis is placed on newer emerging diagnostic and therapeutic applications, such as microwave breast cancer detection, separation of red cells, bio-detection devices, hyperthermia treatment of tumors, and treatment with localized high power used in ablation of the heart, and liver and others. A very brief outline of biological effects of microwaves and associated issues is given as background to the applications.

Investigation of SAR compression technique for point target

The essence of remote sensing resides in the acquisition of information about remote targets for further processing. Synthetic Aperture Radar (SAR) has evolved as a powerful tool that accomplishes the necessities of remote sensing plus some additional characteristics such as day-night all-weather operation and good resolution. These characteristics make SAR a very attractive tool in remote sensing but a very expensive operation from the point of view of computational processing and storage costs. Based on advances in signal processing and image processing, for example, fast Fourier Transform (FFT), correlation and convolution techniques, an environment for SAR processing has been developed and constitute the work reported in this paper. The environment includes implementation of one of the SAR algorithms meant for Point target. A MATLABreg based environment is presented for signal processing. Special attention is given to the development of algorithm for image formation from raw data.

Synthetic Aperture Radar Image Processing Based on Piecewise Linear Mapping

Block adaptive quantization (BAQ) has been an optimum method of SAR raw data compression technique for quite long time due to its simplicity in implementation and results but its performance deteriorates as the saturation level of the data input increased. In order to overcome this drawback, the author has studied the mapping between the average signal magnitude (ASM) and the standard deviation of the input signal (SDIS). We also evaluated the mapping between the ASM and SDOS from the A/D. Monte-Carlo experiment shows that none of the above two mappings is the optimal in the whole set of SD. Thus, this paper proposes the concept of piecewise linear mapping and the searching algorithm in the whole set of SD. According to the linear part, this paper gives the certification and analytical value of k and for nonlinear part, and utilizes the searching algorithm mentioned above to search the corresponding value of k. Results obtained from simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is having heavy SD.

Role of Microwave Imaging for Early Detection of Breast Cancer

Active microwave imaging methods are explored and reviewed as imaging modalities to detect early signs of cancerous tumors in the breasts. The physical basis for microwave imaging lies in the significant contrast in the dielectric properties between the normal breast tissue and the malignant tissue at microwave frequencies [1]-[5]. These imaging methods are described to suggest improvements on early signs of breast cancer detection on subjects by using various microwave methods to examine the dielectric responses and properties of breast tissues and to construct images from the information gathered. The methods researched allow for earlier detection of cancerous tumors than the current methods that detect cancers symptomatically. Results of the research will aid the hypothesis that microwave breast cancer detection can become a flourishing clinical complement to the conventional methods of mammography.

Back propagation neural network approach for SAR raw data compression

Synthetic aperture radar (SAR) is a coherent active and high-resolution microwave imaging system with diverse applications in remote sensing. A significant characteristic of this system is the generation of a large amount of data that involves major problems related to on-board data storage. The near future SAR satellite missions planned would be pushing downlink data bandwidth to prohibitive levels. Given the unprecedented volume of data that will be generated by future high-resolution SAR satellites, the use of innovative data compression techniques will be essential if economically feasible. It is proposed to first pre-process the raw data and then to apply a suitable compression technique like back-propagation neural network whose on-board implementation would be efficient both in terms of speed and power.

SAR polar format implementation with MATLAB

Transform coding based on the Karhunen-Loeve transform (KLT), the discrete cosine transform (DCT), and the discrete wavelet transform (DWT) is well-understood for optical images. Transform coding applied to synthetic aperture radar (SAR) data, however, has not been well studied. This paper shows the results of compressing SAR images when it is available in polar formats. We compare the compression results based on six performance criteria-mean-squared error (MSE), mean absolute error, peak signal-to-noise ratio (SNR), energy compaction, transform gain, and compression ratio (CR).