An Overview of Technological Revolution in Satellite Image Analysis <s> Introduction <s> This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Introduction <s> Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration. <s> BIB002 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Introduction <s> The satellite images are applied on a wide range of applications. It plays a vital role in monitoring and maintaining the environmental changes and also in many agricultural applications. Nowadays, the Land degradation, Ground water scarcity and other climatic hazards are intensively high and it needs development of more advanced prediction models. These models can be deployed more effectively on using satellite images. Due to the launch of many modern satellites the availability of satellite images are high but still the exploitation of these resources are very scarce. It is due to the lack of knowledge over varied properties and the availability of different sources of satellite images. Though the moderate resolution satellite images are freely available, the rate of exploitation of these resources is still very less. Hence, this paper focuses on detailing the diverse characteristics and availability of satellite images which could be a great help for beginners in remote sensing research. In addition the details on major satellite derived products which are applied widely in different mapping applications are also listed. Further, the importance of satellite images in few notable applications is also explained. <s> BIB003
The remote sensing is a field of acquiring the information of an object through sensing either own or artificially emitted electromagnetic radiation. It can be either passive or active means of remote sensing. In passive mode of remote sensing, the sensors detect naturally available energy of an object due to sunlight and other infrared radiations of the electromagnetic spectrum reflected from the earth surface as in Fig.1.(a) . In active remote sensing, the sensors generate an artificial radiation to monitor an object on the earth surface as in Fig.1 .(b). These observed signals are then digitized as satellite images in the ground stations. The passive sensors are limited on night sensing due to lack of sunlight. But active sensors are more advantageous which can be acquired all through the day and even under heavy cloud and fog conditions  . The Satellite images are widely used for better visualization of distant objects, weather prediction models and other spatial object recognition models. As the field of remote sensing is highly associated with observing the real life events, the human intelligence based ML methods can be of great benefit in discriminating or monitoring the dayto-day life on earth. The satellite sensor variations such as spatial, spectral and radiometric resolution along with change of viewpoint bring huge diversities into satellite image representations BIB003 . The fusion techniques can integrate images of the above said different variations and could enrich the information available of the chosen study area. Le Yu et al., in 2008 performed a completely automatic registration in multi sensor satellite images using point features BIB002 . Barbara Zitova et al, in 2003 made an extensive survey on available image registration methods BIB001 . But in case of large areas, it is still a laborious task to apply image fusion. The conventional satellite image models are mostly based on statistical methods like maximum likelihood approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and other regression based models etc-. Most of these traditional algorithms suffer with lack of logical reasoning. By incorporating human level reasoning into these traditional models, a vibrant progress has been achieved in ML methods. But supervised ML methods suffer with high computational complexity due to tedious training processes and insufficient ground truth data for labeling. In order to overcome these issues, the training phase must be limited which can be achieved through unsupervised mode of learning. Further, the accuracy of classification either in supervised or unsupervised can be improved using hybridization. Similarly, parallel processing of data which could improve the issues on speed of computation through higher level Graphical Processing Unit (GPU) architectures is in progress  . The most encouraging factor of satellite image analysis is the involvement of different domain experts and it has been portrayed in Section 2. The extensive applications based on statistical and ML methods to exploit satellite images are detailed in the Sections 3 and 4 respectively.