SAR Image Change Detection via Multiple-Window Processing with Structural Similarity

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.

Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window Matching Results

Traditional stereo dense image matching (DIM) methods normally predefine a fixed window to compute matching cost, while their performances are limited by the matching window sizes. A large matching window usually achieves robust matching results in weak-textured regions, while it may cause over-smoothness problems in disparity jumps and fine structures. A small window can recover sharp boundaries and fine structures, while it contains high matching uncertainties in weak-textured regions. To address the issue above, we respectively compute matching results with different matching window sizes and then proposes an adaptive fusion method of these matching results so that a better matching result can be generated. The core algorithm designs a Convolutional Neural Network (CNN) to predict the probabilities of large and small windows for each pixel and then refines these probabilities by imposing a global energy function. A compromised solution of the global energy function is utilized by breaking the optimization into sub-optimizations of each pixel in one-dimensional (1D) paths. Finally, the matching results of large and small windows are fused by taking the refined probabilities as weights for more accurate matching. We test our method on aerial image datasets, satellite image datasets, and Middlebury benchmark with different matching cost metrics. Experiments show that our proposed adaptive fusion of multiple-window matching results method has a good transferability across different datasets and outperforms the small windows, the median windows, the large windows, and some state-of-the-art matching window selection methods.

Study of Optimized Window Aggregate Function for Big Data Analytics

Background: A Window Aggregate function belongs to a class of functions, which have emerged as a very important tool for Big Data Analytics. They lend support in analysis and decisionmaking applications. A window aggregate function aggregates and returns the result by applying the function over a limited number of tuples corresponding to current tuple and hence lending support for big data analytics. We have gone through different patents related to window aggregate functions and its optimization. The cost associated with Big data analytics, especially the processing of window functions is one of the major limiting factors. However, now a number of optimizing techniques have evolved for both single as well as multiple window aggregate functions. Methods: In this paper, the authors have discussed various optimization techniques and summarized the latest techniques that have been developed over a period through intensive research in this area. The paper tried to compare various techniques based on certain parameters like the degree of parallelism, multiple window function support, execution time etc. Results: After analyzing all these techniques, segment tree data structure seems better technique as it outperforms other techniques on different grounds like efficiency, memory overhead, execution speed and degree of parallelism. Conclusion: In order to optimize the window aggregate function, segment tree data structure technique is a better technique, which can certainly improve the processing of window aggregate function specifically in big data analytics.