4 research outputs found
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
Sensor Signal and Information Processing II
In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing
Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging
Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples
Target recognition techniques for multifunction phased array radar
This thesis, submitted for the degree of Doctor of Philosophy at University College London, is a
discussion and analysis of combined stepped-frequency and pulse-Doppler target recognition methods
which enable a multifunction phased array radar designed for automatic surveillance and multi-target
tracking to offer a Non Cooperative Target Recognition (NCTR) capability. The primary challenge
is to investigate the feasibility of NCTR via the use of high range resolution profiles. Given stepped
frequency waveforms effectively trade time for enhanced bandwidth, and thus resolution, attention is
paid to the design of a compromise between resolution and dwell time. A secondary challenge is to
investigate the additional benefits to overall target classification when the number of coherent pulses
within an NCTR wavefrom is expanded to enable the extraction of spectral features which can help
to differentiate particular classes of target. As with increased range resolution, the price for this extra
information is a further increase in dwell time. The response to the primary and secondary challenges
described above has involved the development of a number of novel techniques, which are summarized
below:
ā¢ Design and execution of a series of experiments to further the understanding of multifunction
phased array Radar NCTR techniques
ā¢ Development of a āHybridā stepped frequency technique which enables a significant extension
of range profiles without the proportional trade in resolution as experienced with āClassicalā
techniques
ā¢ Development of an āend to endā NCTR processing and visualization pipeline
ā¢ Use of āDoppler fractionā spectral features to enable aircraft target classification via propulsion
mechanism. Combination of Doppler fraction and physical length features to enable broad
aircraft type classification.
ā¢ Optimization of NCTR method classification performance as a function of feature and waveform
parameters.
ā¢ Generic waveform design tools to enable delivery of time costly NCTR waveforms within operational
constraints.
The thesis is largely based upon an analysis of experimental results obtained using the multifunction
phased array radar MESAR2, based at BAE Systems on the Isle of Wight. The NCTR
mode of MESAR2 consists of the transmission and reception of successive multi-pulse coherent bursts
upon each target being tracked. Each burst is stepped in frequency resulting in an overall bandwidth
sufficient to provide sub-metre range resolution. A sequence of experiments, (static trials, moving
point target trials and full aircraft trials) are described and an analysis of the robustness of target
length and Doppler spectra feature measurements from NCTR mode data recordings is presented. A
recorded data archive of 1498 NCTR looks upon 17 different trials aircraft using five different varieties
of stepped frequency waveform is used to determine classification performance as a function of
various signal processing parameters and extent (numbers of pulses) of the data used. From analysis
of the trials data, recommendations are made with regards to the design of an NCTR mode for an
operational system that uses stepped frequency techniques by design choice