Design and Implementation of the Image Processing Algorithm Framework for Remote Sensing

Nowadays, the increase of quality and resolution of remote sensing image will lead to the data's capacity extremely increase; A single computer, even a very expensive minicomputer or a PC server, is difficult to meet the disk space, memory space and processing time requirements when faced with this situation. Cloud computing can give us a new resolution to these problems. In this study, we proposed a Hadoop-based remote sensing image processing system (HBRSIPS), on the one hand, it re-defines a storage structure to adapt the characters of remote sensing images, on the other hand, We construct Java program classes based on MapReduce architecture, remote sensing image processing algorithms can be easily applied to the cloud computing environment. The experiments show, the proposed system can save remote sensing image into Hadoop's distribute storage environment and algorithms based on cloud environment were faster than single process algorithm.

This paper mainly studies the low-altitude UAV (unmanned aerial vehicle) remote sensing digital processing system. The system is mainly a set of digital remote sensing image processing system developed for the data acquired by the system. First, it describes the basic principles of photogrammetry on which the system is based, and mainly explains the advantages of the beam method area network adjustment. This article briefly describes the common methods of surveying technology for power transmission and transformation lines. UAV aerial survey technology and GIS platform are applied to electric power surveys; the UAV aerial survey data are used as the data source to establish a GIS database, and the detailed system design of the structure, interface, and database of the electric power survey geographic information system is carried out. The secondary development realizes the main functions of the system such as data management, spatial query, surface analysis, and spatial analysis, facilitating scientific information management and comprehensive analysis, providing auxiliary decision-making, and bringing new methods to electric power surveys. Secondly, this article points out that the system uses an ordinary digital camera with small image size and large distortion. This article is based on the basic principles of photogrammetry, and it is worthwhile to use navigation GPS. Thirdly, the accuracy of the aerial triangulation results of the system and the accuracy of the digital orthophoto were analyzed through a large amount of data.

A new remote sensing image processing system's algorithm module has been introduced in this paper, which is coded with Visual C++ 6.0 program language and can process large volume of remote sensing image. At the same time, adopted key technologies in algorithm module are given. Two defects of American remote sensing image processing system called ERDAS has been put forward in image filter algorithm and the storage of pixel values that are out of data type range. In author's system two optimized methods has been implemented in these two aspects. By contrasted with ERDAS IMAGINE System, the two methods had been proved to be effective in image analysis.

SIGNAL PROCESSING FOR REMOTE SENSING On the Normalized Hilbert Transform and Its Applications in Remote Sensing Steven R. Long and Norden E. Huang Statistical Pattern Recognition and Signal Processing in Remote Sensing Chi Hau Chen A Universal Neural Network-Based Infrasound Event Classifier Fredric M. Ham and Ranjan Acharyya Construction of Seismic Images by Ray Tracing Enders A. Robinson Multi-Dimensional Seismic Data Decomposition by Higher Order SVD and Unimodal ICA Nicholas Le Bihan, Valeriu Vrabie, and Jerome I. Mars Application of Factor Analysis in Seismic Profiling Zhenhai Wang and Chi Hau Chen Kalman Filtering for Weak Signal Detection in Remote Sensing Stacy L. Tantum, Yingyi Tan, and Leslie M. Collins Relating Time-Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics J. Verbesselt, P. Joensson, S. Lhermitte, I. Jonckheere, J. van Aardt, and P. Coppin Use of a Prediction-Error Filter in Merging Highand Low-Resolution Images Sang-Ho Yun and Howard Zebker Blind Separation of Convolutive Mixtures for Canceling Active Sonar Reverberation Fengyu Cong, Chi Hau Chen, Shaoling Ji, Peng Jia, and Xizhi Shi Neural Network Retrievals of Atmospheric Temperature and Moisture Profiles from High-Resolution Infrared and Microwave Sounding Data William J. Blackwell Satellite-Based Precipitation Retrieval Using Neural Networks, Principal Component Analysis, and Image Sharpening Frederick W. Chen IMAGE PROCESSING FOR REMOTE SENSING Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface Dale L. Schuler, Jong-Sen Lee, and Dayalan Kasilingam MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation Sebastiano B. Serpico and Gabriele Moser Random Forest Classification of Remote Sensing Data Sveinn R. Joelsson, Jon Atli Benediktsson, and Johannes R. Sveinsson Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning Ryuei Nishii and Shinto Eguchi Unsupervised Change Detection in Multi-Temporal SAR Images Lorenzo Bruzzone and Francesca Bovolo Change-Detection Methods for Location of Mines in SAR Imagery Maria Tates, Nasser Nasrabadi, Heesung Kwon, and Carl White Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data Jose M.B. Dias and Jose M.P. Nascimento Two ICA Approaches for SAR Image Enhancement Chi Hau Chen, Xianju Wang, and Salim Chitroub Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery Massimo Bertacca, Fabrizio Berizzi, and Enzo Dalle Mese Spatial Techniques for Image Classification Selim Aksoy Data Fusion for Remote-Sensing Applications Anne H.S. Solberg The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing Boris Escalante-Ramirez and Alejandra A. Lopez-Caloca Multi-Sensor Approach to Automated Classification of Sea Ice Image Data A.V. Bogdanov, S. Sandven, O.M. Johannessen, V.Yu. Alexandrov, and L.P. Bobylev Use of the Bradley-Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image Alfred Stein, Gerrit Gort, and Arko Lucieer SAR Image Classification by Support Vector Machine Michifumi Yoshioka, Toru Fujinaka, and Sigeru Omatu Quality Assessment of Remote Sensing Multi-Band Optical Images Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Massimo Selva Index

The growth of the Web has resulted in the Web‐based sharing of distributed geospatial data and computational resources. The Geospatial Processing Web (GeoPW) described here is a set of services that provide a wide array of geo‐processing utilities over the Web and make geo‐processing functionalities easily accessible to users. High‐performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high‐performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi‐granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI‐oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub‐models. In addition, interfaces, definitions, parallel task scheduling and fault‐tolerance mechanisms are described in detail. Based on the model and methods, we propose an open‐source online platform named OpenRS‐Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi‐granularity parallel model for unified remote sensing image processing web services.

Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters.

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

Remote-sensing image processing applications demand large-scale, collaborative processing and storage capabilities. Computational grid can provide abundant powerful computational resources with sharing and cooperation in grid environment. This paper gives an introduction to a collaborative processing mechanism based on workflow technology implemented in ImageGrid. The main idea is to encapsulate all parallel algorithms of remote-sensing image processing into grid services and recombine them with grid workflow to satisfy more and more complex remote-sensing image processing applications. In particular, we describe the architecture of the workflow-based grid system and the processing flow. We also give out the algorithm classification and an example of remote-sensing image processing grid application.

A benchmark data set for aircraft type recognition from remote sensing images

Image processing and computer vision algorithms are very computationally intensive and could not be implemented on low power microcontroller used in embedded and small IoT devices. Performing computation on the cloud is not practical for always-on real-time operations and deploying a high-end microcontroller or a microprocessor consume too much power and are generally too expensive to fit into these small systems. In this paper, we propose a new programming workflow using reusable hardware modules and a graphical programming interface for implementing a complete image processing system on an FPGAs which overcomes the steep learning curve of tradition FPGAs design tool. The design can be deployed onto the low-ended FPGAs in a similar price point as a mid-range microcontroller. To demonstrate our proposed framework, we have implemented a number of image transformation operations on an FPGAs development board with Lattice iCE40 Ultra Plus FPGA and a tiny camera module. Results have shown that our designs can fit in a low-ended FPGAs while performing 5.5-345x faster and consuming 2.4-4x less power compared to current state-of-the-art microcontroller used in small embedded and IoT devices.

Modern remote sensing (RS) image application systems often distribute image processing tasks among multiple data centers and then gather the processed images from each center to efficiently synthesize the final product. In this paper, we exploit the edge-cloud architecture to design and implement a novel RS image service system, called RS-pCloud, which leverages the Peer-to-Peer (P2P) model to integrate multiple data centers and their associated edge networks. The data center as cloud platform is responsible for the storage and processing of original RS images, as well as the storage of partial processed images while the edge network is mainly for caching and sharing the processed images. With this design, RS-pCloud not only achieves the load sharing of processing works but also attains the data efficiency among the edges at the same time, which in turn improves the performance of the image processing and reduce the cost of the data transmission as well. RS-pCloud is designed to be used in a transparent way where it receives a query task from the user through a certain cloud platform, split the task into different sub-tasks, according to the location of the data they required, and then distribute the sub-tasks to corresponding clouds for near-data processing, the returned results from each cloud are first cached in specific edge for further sharing and then gathered at the client to synthesize the final product. We implemented and deployed RS-pCloud on three clusters in conjunction with an edge network to show its performance advantages over traditional single-cluster systems.

Aircraft detection is the challenging task in optical remote sensing image processing system. Due to the shadow affection, the accuracy of detection results getting lower. Therefore, a method of the land aircraft detection which based on shadow affection in optical remote sensing image has been proposed. First, utilizing the anisotropic diffusion method to filter the noise in image, and analyzing the characteristics of gray-scale image to locate shadow regions and candidate target areas. Then, segmentation is performed on the filter able image to highlight the target. Next, we use the morphology operation to strengthen the target area connectedness. Finally, making full use of the aircraft features for target representation, it will reflecting the aircraft target prior-knowledge, at the same time, the accuracy of detection results will be greatly enhancement. Experimental results reveal the feasibility and validity of the proposed approach in detection aircrafts in optical remote sensing images.

This study aimed to evaluate the landslide risk map in the Algerian Western coasts. This evaluation was based on three steps. The first step requires evaluating the landslide hazard. To reach this, a field surveys data, combined with Geographical Information System (GIS) analysis and Remote Sensing (RS) image processing were carried out. Seven controlling factors were considered: lithology, geomorphology, slope, land use, distance to stream, rainfall and distance to fault. A topographic map of 1/ 25 000 was used to generate a Digital Elevation Model (DEM) with 15 × 15 m of resolution. From this DEM, the slope was extracted. Based on knowledge approach, the different factors were weighted according a scale value ranging from 1 to 9. The lowest values were assigned to the factors which have a minor influence on landslide triggering, and the highest values were given to the important parameters for landslide occurrence. These factors were combined using weighted linear combination (WLC). The landslide hazard map was classified into five levels: very low, low, moderate, high and very high. The landslide vulnerability was evaluated through the identification of the elements at risk. Three vulnerabilities aspects were considered: physical, environmental and socio-economic. The weights of each factor were given depending on the magnitude and the rate of landslide. Landslide Vulnerability Map (LVM) for Algerian western coasts was generated by the combination of the physical, environment and socio-economic vulnerability maps. Landslide risk was evaluated by combining the hazard map and the vulnerability map, and it was divided into four classes: very low, low, moderate and high.

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

In portable precision visual measurement, processing result of the measurement image has an important influence on final measurement accuracy in large field working space. However, the images taken in large field working space always have the following characteristics: containing a large amount of information, having a high redundancy and including a lot of background noises. In order to solve these problems, precision image processing and recognition algorithm is researched. Firstly, portable precision visual measurement method is introduced. Then, the design method of global control points including noncoding points and coding points is displayed. Finally, the image processing and recognition algorithm is investigated in detail, including the largest variance threshold method used to partly binary the image, the detection algorithm of global control points based on the constraint of distance and acreage and coding value recognition method through correcting the image feature. A large amount of experiments show that the image processing and recognition algorithm is accurate and effective to processing the image taken in the working space.