33 research outputs found
Non-Parametric Spatial Spectral Band Selection methods
© Cranfield University 2021. All rights reserved. No part of this
publication may be reproduced without the written permission of the
copyright ownerThis project is about the development of band selection (BS) techniques for better
target detection and classification in remote sensing and hyperspectral imaging
(HSI). Conventionally, this is achieved just by using the spectral features for
guiding the band compression. However, this project develops a BS method
which uses both spatial and spectral features to allow a handful of crucial spectral
bands to be selected for enhancing the target detection and classification
performances.
This thesis firstly outlines the fundamental concepts and background of remote
sensing and HSI, followed by the theories of different atmospheric correction
algorithms — in order to assess the reflectance conversion for band selection —
and BS techniques, with a detailed explanation of the Hughes principle, which
postulates the fundamental drawback for having high-dimensional data in HSI.
Subsequently, the thesis highlights the performances of some advanced BS
techniques and to point out their deficiencies. Most of the existing BS work in the field have exhibited maximal classification
accuracy when more spectral bands have been utilized for classification; this
apparently disagrees with the theoretical model of the Hughes phenomenon. The
thesis then presents a spatial spectral mutual information (SSMI) BS scheme
which utilizes a spatial feature extraction technique as a pre-processing step,
followed by the clustering of the mutual information (MI) of spectral bands for
enhancing the BS efficiency. Through this BS scheme, a sharp ’bell’-shaped
accuracy-dimensionality characteristic has been observed, peaking at about 20
bands.
The performance of the proposed SSMI BS scheme has been validated through
6 HSI datasets, and its classification accuracy is shown to be ~10% better than 7
state-of-the-art BS algorithms. These results confirm that the high efficiency of
the BS scheme is essentially important to observe, and to validate, the Hughes
phenomenon at band selection through experiments for the first time.PH
Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System
Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
As an emerging technology, hyperspectral imaging provides huge
opportunities in both remote sensing and computer vision. The
advantage of hyperspectral imaging comes from the high resolution
and wide range in the electromagnetic spectral domain which
reflects the intrinsic properties of object materials. By
combining spatial and spectral information, it is possible to
extract more comprehensive and discriminative representation for
objects of interest than traditional methods, thus facilitating
the basic pattern recognition tasks, such as object detection,
recognition, and classification. With advanced imaging
technologies gradually available for universities and industry,
there is an increased demand to develop new methods which can
fully explore the information embedded in hyperspectral images.
In this thesis, three spectral-spatial feature extraction methods
are developed for salient object detection, hyperspectral face
recognition, and remote sensing image classification.
Object detection is an important task for many applications based
on hyperspectral imaging. While most traditional methods rely on
the pixel-wise spectral response, many recent efforts have been
put on extracting spectral-spatial features. In the first
approach, we extend Itti's visual saliency model to the spectral
domain and introduce the spectral-spatial distribution based
saliency model for object detection. This procedure enables the
extraction of salient spectral features in the scale space, which
is related to the material property and spatial layout of
objects.
Traditional 2D face recognition has been studied for many years
and achieved great success. Nonetheless, there is high demand to
explore unrevealed information other than structures and textures
in spatial domain in faces. Hyperspectral imaging meets such
requirements by providing additional spectral information on
objects, in completion to the traditional spatial features
extracted in 2D images. In the second approach, we propose a
novel 3D high-order texture pattern descriptor for hyperspectral
face recognition, which effectively exploits both spatial and
spectral features in hyperspectral images. Based on the local
derivative pattern, our method encodes hyperspectral faces with
multi-directional derivatives and binarization function in
spectral-spatial space. Compared to traditional face recognition
methods, our method can describe distinctive micro-patterns which
integrate the spatial and spectral information of faces.
Mathematical morphology operations are limited to extracting
spatial feature in two-dimensional data and cannot cope with
hyperspectral images due to so-called ordering problem. In the
third approach, we propose a novel multi-dimensional morphology
descriptor, tensor morphology profile~(TMP), for hyperspectral
image classification. TMP is a general framework to extract
multi-dimensional structures in high-dimensional data. The
n-order morphology profile is proposed to work with the n-order
tensor, which can capture the inner high order structures. By
treating a hyperspectral image as a tensor, it is possible to
extend the morphology to high dimensional data so that powerful
morphological tools can be used to analyze hyperspectral images
with fused spectral-spatial information.
At last, we discuss the sampling strategy for the evaluation of
spectral-spatial methods in remote sensing hyperspectral image
classification. We find that traditional pixel-based random
sampling strategy for spectral processing will lead to unfair or
biased performance evaluation in the spectral-spatial processing
context. When training and testing samples are randomly drawn
from the same image, the dependence caused by overlap between
them may be artificially enhanced by some spatial processing
methods. It is hard to determine whether the improvement of
classification accuracy is caused by incorporating spatial
information into the classifier or by increasing the overlap
between training and testing samples. To partially solve this
problem, we propose a novel controlled random sampling strategy
for spectral-spatial methods. It can significantly reduce the
overlap between training and testing samples and provides more
objective and accurate evaluation
Object detection and classification in aerial hyperspectral imagery using a multivariate hit-or-miss transform
High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques
Application of Multi-Sensor Fusion Technology in Target Detection and Recognition
Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Hyperspectral imaging (HSI) is a non-destructive and contactless technology
that provides valuable information about the structure and composition of an
object. It can capture detailed information about the chemical and physical
properties of agricultural crops. Due to its wide spectral range, compared with
multispectral- or RGB-based imaging methods, HSI can be a more effective tool
for monitoring crop health and productivity. With the advent of this imaging
tool in agrotechnology, researchers can more accurately address issues related
to the detection of diseased and defective crops in the agriculture industry.
This allows to implement the most suitable and accurate farming solutions, such
as irrigation and fertilization before crops enter a damaged and
difficult-to-recover phase of growth in the field. While HSI provides valuable
insights into the object under investigation, the limited number of HSI
datasets for crop evaluation presently poses a bottleneck. Dealing with the
curse of dimensionality presents another challenge due to the abundance of
spectral and spatial information in each hyperspectral cube. State-of-the-art
methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and
spatial information. On the other hand, 3D-CNN-based models have shown
significant promise in achieving better classification and detection results by
leveraging spectral and spatial features simultaneously. Despite the apparent
benefits of 3D-CNN-based models, their usage for classification purposes in
this area of research has remained limited. This paper seeks to address this
gap by reviewing 3D-CNN-based architectures and the typical deep learning
pipeline, including preprocessing and visualization of results, for the
classification of hyperspectral images of diseased and defective crops.
Furthermore, we discuss open research areas and challenges when utilizing
3D-CNNs with HSI data