Remote Sensing Image Processing Applications Research Articles

Automatic Registration of Remote Sensing Images Based on Revised SIFT With Trilateral Computation and Homogeneity Enforcement

Automatic registration of remote sensing images is an essential task that requires the establishment of appropriate correspondences between the sensed image and the reference image. Among the feature-based matching approaches, attempts relied on the scale-invariant feature transform (SIFT) algorithm have shown particular superiority over other methods. From the perspective of automatic registration, the challenges of SIFT-based methods involve the elimination of mismatches and the homogeneity of matches. While many outlier removal methods have been proposed, the strategies for uniform distribution have been lacking. To address these two issues, this article investigates a new remote sensing image registration algorithm based upon a revised SIFT scheme. Additionally, an outlier removal mechanism founded on a trilateral computation (Tc) recipe and a homogeneity enforcement (He) layout according to a divide-and-conquer inclusion tactic are proposed. Finally, a stochastic competition process based upon game theory is introduced to secure an appropriate amount of correct matches in the proposed Tc and He (TcHe)-SIFT framework. A wide variety of multispectral and multitemporal remote sensing images with various scenarios were exploited to evaluate the proposed registration algorithm. Experimental results demonstrated the advantages of our developed remote sensing image registration algorithm over the state-of-the-art SIFT-based methods. We believe that this new registration technique is of potential in a number of remote sensing image processing applications.

Evaluating the attributes of remote sensing image pixels for fast k-meansclustering

Clustering process is an important stage for many data mining applications. In this process, data elements are grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm. The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing image processing applications usually need the clustering stage like many image processing applications. Remote sensing images provide more information about the environments with the development of the multispectral sensor and laser technologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also supplied besides the red (R), the green (G), and the blue (B) color values of the pixels. However, remote sensing images come with very large sizes (6000 $\times$ 6000 pixels for each image in the dataset used). Clustering these large-size images using their multiattributes consumes too much time if it is used directly. In the literature, some studies are available to accelerate the k-means algorithm. One of them is the normalized distance value (NDV)-based fast k-means algorithm that benefits from the speed of the histogram-based approach and uses the multiattributes of the pixels. In this paper, we evaluated the effects of these attributes on the correctness of the clustering process with different color space transformations and distance measurements. We give the success results as peak signal-to-noise ratio and structural similarity index values using two different types of reference data (the source images and the ground-truth images) separately. Finally, we give the results based on accuracy measurement for evaluating both the success of the clustering outputs and the reliability of the NDV-based measurement methods presented in this paper.

Reinforcement learning-driven address mapping and caching for flash-based remote sensing image processing

Abstract Flash memory is featured with salient advantages over conventional hard disks for massive data storage and efficient on-board data processing. A flash translation layer (FTL) is a critical component for flash-based storage devices to handle particular technical constraints of flash. It is desirable to use flash memory for the storage of massive remote sensing images and support on-board remote sensing data processing applications, which typically require high I/O performance and hence call for advanced FTL design and implementations. In this paper, we introduce our efforts in developing a reinforcement learning driven page-level mapping and caching scheme (named Q-FTL) that is adaptive and responsive to ever-changing I/O streams of on-board remote sensing image processing operations. The adaptability and responsiveness are achieved by the separation of large and small I/O requests, an integrated weighting scheme to measure access costs of cached translation pages, and a reinforcement learning driven cache replacement algorithm. We demonstrate the efficiency of the proposed approach using actual I/O traces generated from on-board remote sensing image processing applications. Experimental results show that Q-FTL improves over several current state-of-the-art FTLs by a large margin and even achieves competitive performance close to an idealized pure page mapping FTL in some cases.

PSOSAC: Particle Swarm Optimization Sample Consensus Algorithm for Remote Sensing Image Registration

Image registration is an important preprocessing step for many remote sensing image processing applications, and its result will affect the performance of the follow-up procedures. Establishing reliable matches is a key issue in point matching-based image registration. Due to the significant intensity mapping difference between remote sensing images, it may be difficult to find enough correct matches from the tentative matches. In this letter, particle swarm optimization (PSO) sample consensus algorithm is proposed for remote sensing image registration. Different from random sample consensus (RANSAC) algorithm, the proposed method directly samples the modal transformation parameter rather than randomly selecting tentative matches. Thus, the proposed method is less sensitive to the correct rate than RANSAC, and it has the ability to handle lower correct rate and more matches. Meanwhile, PSO is utilized to optimize parameter as its efficiency. The proposed method is tested on several multisensor remote sensing image pairs. The experimental results indicate that the proposed method yields a better registration performance in terms of both the number of correct matches and aligning accuracy.

Parallel programing templates for remote sensing image processing on GPU architectures: design and implementation

Remote sensing image processing is characterized with features of massive data processing, intensive computation, and complex processing algorithms. These characteristics make the rapid processing of remote sensing images very difficult and inefficient. The rapid development of general-purpose graphic process unit (GPGPU) computing technology has resulted in continuous improvement in GPU computing performance. Its strong floating point calculating capability, high intensive computation, small volume, and excellent performance-cost ratio provide an effective solution to the problems faced in remote sensing image processing. However, current usage of GPU in remote sensing image processing applications has been limited to specific parallel algorithms and their optimization of processes, rather than formed well-established models and methods. This has introduced serious problems to the development of remote sensing image processing algorithms on GPU architectures. For example, GPU parallel strategies and algorithms are highly coupled and non-reusable. The processing system is closely associated with the GPU hardware so that programming for remote sensing algorithms on GPU is nothing but easy. In this paper, we attempt to explore a reusable GPU-based remote sensing image parallel processing model and to establish a set of parallel programming templates, which provides programmers with a more simple and effective way for programming parallel remote sensing image processing algorithms.

Survey of Support Vector Machine in the Processing of Remote Sensing Image

Support vector machine is a kind of machine learning algorithm which is based on statistical learning theory and VC dimension theory and structural risk minimization principle, it can solve data classification and regression problems. With the in-depth research of support vector machine, in the field of remote sensing image processing applications are also obtained the very big development. This paper first gives a brief introduction of the theory of support vector machine, and then summarized the progress in remote sensing image compression, geometric correction of remote sensing images, processing of remote sensing image data classification research, finally proposed the trend of the support vector machine in application and development in the field of remote sensing image processing problems.