Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

A Hybrid Particle Swarm Optimization with Affine Transformation Approach for Cloud Free Multi-Temporal Image Registration

An image registration is the major part of the image categorization and cluster formation in multi temporal image processing. The images are affected by the different factors such as cloud shadow, water level, building shadows etc. In this paper, an enhanced registration process and the cloud removal technique is proposed for image enhancement. The Daemons, Combined Registration and Segmentation (CRS) approach, Markov Random Field (MRF) approach and Mutual Information (MI) based approaches results in more computational complexity, minimum edge preservation measure (QAB/F) and Mutual Information in image registration. In order to maximize the quality of edge preservation measure and MI with minimum computational time, this paper proposes Particle Swarm Optimization (PSO) based affine transformation technique. The proposed techniques measure and compare the computation time against the number of pixels of an image with the existing methods of CRS and MRF for the number of images. The comparative analysis of QAB/F and MI with the traditional methods of Clock Point -Least Square (CP-LS) and the Multi-Focus Image Fusion (MFIF) and Discrete Wavelet Transform (DWT) is presented to confirm the effective performance. The simulation results of the proposed transformation for registration process confirms the effective image registration in the multi-temporal image processing

Image Segmentation in a Remote Sensing Perspective

Image segmentation is generally defined as the process of partitioning an image into suitable groups of pixels such that each region is homogeneous but the union of two adjacent regions is not, according to a homogeneity criterion that is application specific. In most automatic image processing tasks, efficient image segmentation is one of the most critical steps and, in general, no unique solution can be provided for all possible applications. My thesis is mainly focused on Remote Sensing (RS) images, a domain in which a growing attention has been devoted to image segmentation in the last decades, as a fundamental step for various application such as land cover/land use classification and change detection. In particular, several different aspects have been addressed, which span from the design of novel low-level image segmentation techniques to the de?nition of new application scenarios leveraging Object-based Image Analysis (OBIA). More specifically, this summary will cover the three main activities carried out during my PhD: first, the development of two segmentation techniques for object layer extraction from multi/hyper-spectral and multi-resolution images is presented, based on respectively morphological image analysis and graph clustering. Finally, a new paradigm for the interactive segmentation of Synthetic Aperture Radar (SAR) multi-temporal series is introduced

Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

High resolution urban monitoring using neural network and transform algorithms

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover from space. Satellite observations are carried out regularly and continuously and provide a great deal of information on land cover over large areas. High spatial resolution imagery makes it possible to overcome the “mixed-pixel” problem inherent in more moderate resolution satellite sensors. At the same time, high-resolution images present a new challenge over other satellite systems since a relatively large amount of data must be analyzed, processed, and classified in order to characterize land cover features and to produce classification maps. Actually, in spite of the great potential of remote sensing as a source of information on land cover and the long history of research devoted to the extraction of land cover information from remotely sensed imagery, many problems have been encountered, and the accuracy of land cover maps derived from remotely sensed imagery has often been viewed as too low for operational users. This study focuses on high resolution urban monitoring using Neural Network (NN) analyses for land cover classification and change detection, and Fast Fourier Transform (FFT) evaluations of wavenumber spectra to characterize the spatial scales of land cover features. The contributions of the present work include: classification and change detection for urban areas using NN algorithms and multi-temporal very high resolution multi-spectral images (QuickBird, Digital Globe Co.); development and implementation of neural networks apt to classify a variety of multi-spectral images of cities arbitrarily located in the world; use of different wavenumber spectra produced by two-dimensional FFTs to understand the origin of significant features in the images of different urban environments subject to the subsequent classification; optimization of the neural net topology to classify urban environments, to produce thematic maps, and to analyze the urbanization processes. This work can considered as a first step in demonstrating how NN and FFT algorithms can contribute to the development of Image Information Mining (IMM) in Earth Observation

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model

Laser Scanner Technology

Laser scanning technology plays an important role in the science and engineering arena. The aim of the scanning is usually to create a digital version of the object surface. Multiple scanning is sometimes performed via multiple cameras to obtain all slides of the scene under study. Usually, optical tests are used to elucidate the power of laser scanning technology in the modern industry and in the research laboratories. This book describes the recent contributions reported by laser scanning technology in different areas around the world. The main topics of laser scanning described in this volume include full body scanning, traffic management, 3D survey process, bridge monitoring, tracking of scanning, human sensing, three-dimensional modelling, glacier monitoring and digitizing heritage monuments