Acurracy and Quality Assesment of Image Using CURVELET Transform And Minimum Distance Classification Method.do

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. Earth observation satellites provide data covering different portions of the electromagnetic spectrum at different spatial, temporal and spectral resolutions. For the full exploitation of increasingly sophisticated multisource data, advanced analytical or numerical data fusion techniques are being developed. Fused images may provide increased interpretation capabilities and more reliable results since data with different characteristics are combined. The images vary in spectral, spatial and temporal resolution and therefore give a more complete view of the observed objects. It is the aim of image fusion to integrate different data in order to obtain more information than can be derived from each of the single sensor data alone It is a current need of research to extensively use the freely available satellite images. The most commonly available satellite images are Moderate Resolution Imaging Spectroradiometer (MODIS) and The Advanced Very High Resolution Radiometer (AVHRR). The problems with these images are their poor spatial resolution that restricts their use in various applications. This restriction may be minimized by application of the fusion techniques where high resolution image will be used to fuse with low resolution images. Another important aspect of fusion of different sensors data as optical and radar images (where both can provide the complimentary information), and the resultant fused image after fusion may give enhanced and useful information that may be beneficial for various application. Therefore, in this paper an attempt has been made to fuse the full polarimetric Phased Array type L-band SAR(PALSAR) image with MODIS image and assess the quality of fused image. PALSAR image has a advantage of availability of data in four different channels. These four channels are HH (Transmitted horizontal polarization and received also in horizontal polarization), HV (Transmitted horizontal polarization and received vertical polarization), VH (Transmitted vertical polarization and received horizontal polarization) and VV (Transmitted vertical polarization and received vertical polarization), which provides various landcover information. The three major land covers agriculture, urban and water are considered for evaluation of fusion of these images for the Roorkee area of India. The results are quite encouraging, and in near future it may provide a better platform for maximize the use of MODIS images. Keyword : CURVELET TRANSFORM , MINIMUM DISTANCE CLASSIFICATION METHOD II. LITERATURE REVIEW A lot of literature says that image fusion have produced a variety of approaches like image overlay, image sharpening, and image cueing through pixel, feature, or region/shape combinations. The applicability of these approaches and techniques differ on the image content, contextual information, and generalized metrics of image fusion gain. An image fusion gain can be assessed relative to information gain or entropy reduction. Myungjin Choi, Rae Young Kim, Myeong-Ryong Nam, and Hong Oh Kim describes the image fusion using the curvelet transform. A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. The results of most wavelet-based methods of image fusion have a spatial resolution that is less than that obtained via the Brovey, IJAEEE ,Volume1,Number 2 Prerana et al. ISSN:2319-1112 / V1N2:37-50 ©IJAEEE intensity–hue–saturation, and principal components analysis methods of image fusion. Concept of using the curvelet transform, because the curvelet transform represents edges better than wavelets. Because edges are fundamental in image representation, enhancing the edges is an effective means of enhancing spatial resolution. Image fusion is and will be an integral part of many existing and future surveillance systems. However, little or no systematic attempt has been made up to now on studying the relative merits of various fusion techniques and their effectiveness on real multi-sensor imagery. It is a current need of research to extensively use the freely available satellite images. The most commonly available satellite images are Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). The problems with these images are their poor spatial resolution that restricts their use in various applications. This restriction may be minimized by application of the fusion techniques where high resolution image will be used to fuse with low resolution images. A . MODIS AND PALSAR IMAGES THE FUNCTIONAL design of satellite data production systems is based upon the processing of raw instrument data into a hierarchy of increasingly refined data products. These production processes discard large amounts of data throughout the processing chain. This forces the user community to use data that may be inappropriate for their application requirements, precludes opportunities for sophisticated users to take advantage of the entire sensed data set, and makes data reprocessing and on-demand data processing resource intensive. As new remote-sensing systems with improved geometric and radiometric quality and improved calibration stability become available, the requirement for data storage structures that will support flexible application-specific uses of the sensed data will increase. MODIS(or Moderate Resolution Imaging Spectroradiometer) is planned for launch onboard the morning (AM1) and afternoon (PM1) Earth Observing System (EOS) platforms in 1998 and 2000, respectively. MODIS will sense all of the earth’s surface in 36 spectral bands spanning the visible (0.415 m) to infrared (14.235 m) spectrum at nadir spatial resolutions of 1 km, 500, and 250 m. MODIS will provide both day and night full earth coverage every two days and full coverage every day for latitudes above approximately 30. MODIS will be the primary EOS sensor for providing data on global biospheric dynamics and will reduce reliance upon data sensed by instruments such as the Advanced Very High Resolution Radiometer (AVHRR). The MODIS land science team is currently developing remotesensing algorithms for deriving global time-series data products on various terrestrial geophysical parameters that will be used by the earth science community, The products include land surface reflectance, land surface temperature, spectral vegetation indexes, snow and sea ice cover, fire detection, land cover and land cover change, spectral albedo, bidirectional reflectance characterization, and a number of biophysical variables that will contribute to an improved understanding of global carbon cycles, hydrologic balances, and biogeochemical cycles The Phased Array type L-band Synthetic Aperture Radar (PALSAR) is an active microwave sensor using L-band frequency to achieve cloud-free and day-and-night land observation. It provides higher performance than the JERS-1's synthetic aperture radar (SAR). Fine resolution in a conventional mode, but PALSAR will have another advantageous observation mode. Scan SAR, which will enable us to acquire a 250 to 350km width of SAR images (depending on the number of scans) at the expense of spatial resolution. This swath is three to five times wider than conventional SAR images. The development of the PALSAR is a joint project between JAXA and the Japan Resources Observation System Organization (JAROS) PALSAR cannot observe the areas beyond 87.8 deg. north latitude and 75.9 deg. south latitude when the off-nadir angle is 41.5 deg. • Due to power consumption, the operation time will be limited. • Valid for off-nadir angle 34.3 deg. (Fine mode), 34.1 deg. (ScanSAR mode), 21.5 deg. (Polarimetric mode) • S/A level may deteriorate due to engineering changes in PALSAR B. CURVELET TRANSFORM Image Fusion produces a single image by combining information from a set of source images together, using pixel, feature or decision level techniques. The fused image contains greater information content for the scene than any one of the individual image sources alone. The reliability and overall detail of the image is increased, 39 ACURRACY AND QUALITY ASSESMENT OF IMAGE USING CURVELET TRANSFORM AND MINIMUM DISTANCE CLASSIFICATION METHOD ISSN:2319-1112 / V1N2:37-50 ©IJAEEE because of the addition of analogous and complementary information. Image fusion requires that images be registered first before they are fused. We introduce a new image fusion method based on a curvelet transform. The fused image using the curvelet-based image fusion method yields almost the same detail as the original panchromatic image, because curvelets represent edges better than wavelets. It also gives the same colour as the original multispectral images, because we use the wavelet-based image fusion method in our algorithm. As such, this new method is an optimum method for image fusion. The main feature of the curvelet transform is that it is sensitive to directional boundaries and capable of representing the highpass details of object contours at different scales through few sparse nonzero coefficients. The different steps which is used for Curvelet fusion Step 1: ATrous Wavelet Transform Step 2: Ridgelet Transform Step 3: Curvelet Transform C. ATrous WAVELET TRANSFORM The ATrous wavelet transform (ATWT) is a nonorthogonal,multiresolution decomposition defined by a filter bank and { }, with the Kronecker operator denoting an all pass filter. The filter bank does not allow perfect reconstruction to be achieved if the output is decimated. In the absence of decimation, the low pass filter is up sampled by , before processing the jth level; hence the name ‘‘ATrous’’ which means ‘‘with holes’’. In two dimensions, the filter bank becomes and which means that the 2-D detail signals is given by the pixel difference between two successive approximations. For J-level decomposition, the ATWT accommodates a number of coefficients J + 1 times greater than the number of pixels. Due to the absence of decimation, the synthesis is simply obtained by summing details levels to the approximation, thereby the ATWT for the f(m, n) is given by D. RIDGELET AND CURVELET TRANSFORMS Curvelets are a non-adaptive technique for multi-scale object representation. Being an extension of the wavelet concept, they are becoming popular in similar fields, namely in image processing and scientific computing. Wavelets generalize the Fourier transform by using a basis that represents both location and spatial frequency. For 2D or 3D signals, directional wavelet transforms go further, by using basis functions that are also localized in orientation. A curvelet transform differs from other directional wavelet transforms in that the degree of localisation in orientation varies with scale. Curvelets are an appropriate basis for representing images (or other functions) which are smooth apart from singularities along smooth curves, where the curves have bounded curvature, i.e. where objects in the image have a minimum length scale. This property holds for cartoons, geometrical diagrams, and text. As one zooms in on such images, the edges they contain appear increasingly straight. Curvelets take advantage of this property, by defining the higher resolution curvelets to be skinnier the lower resolution curvelets. However, natural images (photographs) do not have this property; they have detail at every scale. Therefore, for natural images, it is preferable to use some sort of directional wavelet transform whose wavelets have the same aspect ratio at every scale.The theory of ridgelet and curvelet transforms, presented are used in image fusion technique E. CONTINOUS RIDGELET TRANSFORM Let ψ be in with sufficient decay and satisfying the admissibility condition, IJAEEE ,Volume1,Number 2 Prerana et al. ISSN:2319-1112 / V1N2:37-50 ©IJAEEE In this process, we adopt Meyer wavelet ψ which has high smoothness and a compact support in the frequency domain. Suppose that ψ is normalized so that =1 For each a > 0, b € R, and Θ € [0; 2π]; ridgelet basis functions are defined by