| dc.description.abstract | It is always an interest of a mankind to explore the various resources of the earth such as minerals, agriculture, forestry, geology, ocean etc., Before the invention of the remote sensing, in order to analyze the various resources it was required to visit the field to take the different forms of data samples and later on those were processed further. The revolution in terms of photography using satellite made it possible to view the earth's surface without being in touch with the area of interest. With the help of satellite technology it is also possible to view the locations on the earth which are not accessible by the mankind. Remote sensing has effectively enabled the mapping, studying, monitoring and management of various resources present on the earth. It has also enabled monitoring of environment and thereby helping in conservation. In the last four decades, the advancement of the remote sensing technologies have improved the methods of collection, processing and analysis of the data.
Remote sensing involves the acquiring of the pictorial data of the earth's surface without any type of physical contact. It provides the information which not only helps in managing and protecting the natural resources but also helpful in the development of land usage in terms of urban planning. One of the major advantages of the remote sensing satellites is the ability to provide the repetitive observations of the same area. This capability is very useful to monitor dynamic phenomena such as cloud evolution, vegetation cover, snow cover, forest _res, etc. A farmer may use thematic maps to monitor the health of his crops without going out to the field. A geologist may use the images to study the types of minerals or rock structure found in a certain area. A biologist may want to study the variety of plants in a certain location.
The image acquisition process of remote sensing system consists of sensing the reflected electromagnetic energy from the surface of the earth. The amount of energy reflected from the earth's surface depends on the composition of the material. The variations in the reflected energy are captured by the remote sensing sensors placed in the satellite or aircraft which are then quantized and digitized into the pictorial form i.e., images. The smallest element of an image i.e., pixel corresponds to an area of a few squared meters in the actual scene which is referred to the spatial resolution of the given sensor. The spatial resolution is limited by the instantaneous field of view (IFOV) of the remote sensing system. Smaller the IFOV, lesser is the area covered by sensor and hence the amount of collected light energy is reduced. By keeping the small IFOV, one can increase the amount of light falling on the sensor i.e. photo detector element by increasing the spectral width of the sensor. This results in wider spectral width with high spatial resolution. Alternatively, one can use the sensor with wide IFOV that covers large surface area. This makes the sensor to collect more light energy but the image formed has lower spatial resolution. However, in this case the spectral width of the sensor can be made narrower in order to sense the data in that spectral width which results in an image with high spectral resolution having fine spectral details. The data with narrower spectral width always helps in better classification since the materials present in the scene reflect the light energy of different wavelengths based on their composition. If one can capture the reflected energy at different bands of wavelengths then it provides separate information about the same scene content. However, this set of images obtained at different spectral bands is possible with the compromise of poor spatial resolution. This trade off in high spatial and spectral resolutions imposes the limitations on the hardware construction in the remote sensing sensors.
It is always of interest to visualize the content of the scene with high spatial and spectral resolutions. However, constraints such as the tradeoff between high spatial and spectral resolutions of the sensor, channel bandwidth, on board storage capability of a satellite system place the limitations on capturing the images with high spectral and spatial resolutions. Due to this, many commercial remote sensing satellites such as Quickbird, Ikonos and Worldview-2 capture the earth's information with two types of images: a single panchromatic (Pan) and a number of multispectral (MS) images. Pan has high spatial resolution with lower spectral resolution while MS image has higher spectral resolving capability with low spatial resolution. An image with high spatial and spectral resolutions i.e., fused image of MS and Pan data can lead to better land classification, map updating, soil analysis, feature extraction etc. Also, since fused image increases the spatial resolution of the MS image it results in sharpening the image content which makes it easy to obtain greater details of the classified maps. The pan-sharpening or multi-resolution image fusion is an algorithmic approach to increase the spatial resolution of the MS image with the preservation of spectral contents by making use of the high spatial resolution Pan image. In this thesis we address some new multi-resolution image fusion techniques.
In multi-resolution image fusion problem, the given MS and Pan images have high spectral and high spatial resolutions, respectively. One can think of obtaining the fused image using these two by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for high frequency details extraction and also on the technique for injecting these details into the MS image. In the literature various approaches have been proposed based on this idea. Motivated from this, we first address the fusion problem by using different edge preserving filters in order to extract the high frequency details from the Pan image. Specifically, we have chosen the guided filter and difference of Gaussians (DoGs) for detail extraction since these are more versatile in applications involving feature extraction, denoising, etc. Using these edge preserving filters we extract the high frequency details from the Pan image and inject them into the upsampled MS image.
One of the drawbacks of the fusion methods using edge preserving filters is the upsampling operation required to perform on the MS image before the injection of high frequency details into the same. Since this operation do not consider the effect of aliasing it results in distortions in the final fused image. Solving the problem of fusion by model based approach is accurate since aliasing present due to undersampled MS observation can be taken care of while modeling. Many researchers have used the model based approaches for fusion with the emphasis on improving the fused image quality and reducing the color distortion. In a model based method, the low resolution (LR) MS image is modeled as the blurred and noisy version of its ideal high resolution (HR) fused image. Since this problem is ill-posed, it requires regularization to obtain the final solution. In the proposed model based approach a learning based method that uses Pan data is used to obtain the required degradation matrix that accounts for aliasing. We use sub-sampled as well as non sub-sampled contourlet transform based learning to obtain close approximation to fused image (initial estimate). Then using the proposed model, the final solution is obtained by solving the inverse problem where a Markov random field (MRF) smoothness prior is used for regularizing the solution.
We next address the fusion problem based on the concept of self similarity and compressive sensing (CS) theory. In the earlier proposed approach, the degradation matrix entries were estimated by modeling the relationship between the Pan derived initial estimate of the fused MS image and LR MS image which may be inaccurate as the estimate depends on the low spectral resolution Pan data. If the initial fused estimate is derived using the available LR MS image only, then the transformation between the estimated high resolution initial estimate and the observed MS image would be more accurate. This makes the estimated degradation matrix to better represent the aliasing. In this case we obtain the initial estimate using the available LR MS image only. Here, we use the property of natural images that the probability of availability of same or similar information in the current resolution and its coarser resolution is high. We exploit this self similarity concept and combine it with CS theory in order to obtain the initial estimate of fused image which is then used in obtaining the degradation. Finally, in order to better preserve the spatial details and to improve the estimate of fused image, we solve the multi-resolution fusion problem in a regularization framework by making use of a new prior called Gabor prior. Use of Gabor prior ensures features at different spatial frequencies of fused image image to match those of the available HR Pan image. Along with Gabor prior we also include a MRF prior which maintains the spatial correlatedness among the HR pixels | |
