# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 1 Introduction  
Demands for high resolution satellite imagery are increasing day by day for more detailed and precise information in land planning, urban planning, and Earth observation. In these applications, satellite image registration is an important pre-processing step. It is a spatial alignment of the same scene acquired from different views and sensors (panchromatic and multi-spectral images), and time intervals. In particular, satellite image registration is a challenging task due to the very-high-resolution (VHR). High-resolution images may occupy over a hundred megabytes with several spectral bands or gigapixels with hyper-spectral bands. Therefore, processing these images with large sizes is difficult due to the limited resources and memory [1; 2; 3]. In the literature, various methods and techniques have been proposed for satellite image registration [1; 4; 5; 6; 7]. In general, the registration methods can be divided into two main groups: area-based and feature-based methods. Area-based methods compare the pixel intensities of the overlapping regions in images by using a similarity measure such as square sum of difference (SSD), mutual information (MI) and/or normalized cross-correlation (NCC) [1; 8]. In these methods, similarity measure plays an important role and the accuracy of the method depends on the selection of an appropriate similarity measure. Usually, these methods provide well-distributed control points;however, these methods suffer from high computational time due to huge numbers of multiplications and divisions. On the other hand, feature-based methods are based on features (points, lines, intersections, etc.) instead of pixel intensities, from the corresponding regions of one or more images [4]. The accuracy of feature-based methods mainly depends on two factors: detected numbers of key-points and the distribution of these key-points over the image. Particularly, a well-distributed set of key-points provide higher accuracy as compared to a set of too clustered points [9; 10]. These method are fast; however, their accuracy depends on the correct matches of features  
In feature-based satellite image registration, scale-invariant feature transform (SIFT) [11; 12] based methods are widely used [13; 14]. A study of performance of local descriptors has revealed that the SIFT provides the best performance [15]. Similar to the other methods, SIFT based methods also suffer from high computational cost and uneven distribution of control points. To reduce the complexity, few attempts have been made by reducing the feature space or minimizing the computations for estimating kernels. [PERSON] and [PERSON] [16] suggested the use of principal component analysis (PCA) to reduce the size of the feature space. [PERSON] et al. [17] suggested another approach, namely speeded up robust features (SURF), which allows fast computation via integral images for extracting control points. These approaches are able to reduce the processing time by compromising on accuracy as these approaches provide less accurate results as compared to the original SIFT based methods [15; 18]. The number of control points for the reference and the sensed images also affects the registration accuracy. In different studies [5; 19], the effect of numbers of control points has been analyzed in terms of the registration accuracy. These studies conclude that a larger number of control points with well-balanced and even distribution provides better registration accuracy. In the case of SIFT based methods, SIFT usually detects larger control points, however, their distribution quality is not enough for accurate registration. Especially, extracted points near the edges are too clustered and in low contrast and smooth areas are too scattered. To overcome this problem, control points from feature-based and area based methods are combined [1; 20]. However, these methods suffer from non-linear similarity among the multi-spectral responses.