401 research outputs found
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely
sensed hyperspectral image (HSI) which integrates spectral and spatial
information in a unified Bayesian framework. First, we formulate the HSI
classification problem from a Bayesian perspective. Then, we adopt a
convolutional neural network (CNN) to learn the posterior class distributions
using a patch-wise training strategy to better use the spatial information.
Next, spatial information is further considered by placing a spatial smoothness
prior on the labels. Finally, we iteratively update the CNN parameters using
stochastic gradient decent (SGD) and update the class labels of all pixel
vectors using an alpha-expansion min-cut-based algorithm. Compared with other
state-of-the-art methods, the proposed classification method achieves better
performance on one synthetic dataset and two benchmark HSI datasets in a number
of experimental settings
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually
chosen and high dimensional in nature. Selecting the most important features is
a very crucial task for systems like facial expression recognition. This paper
investigates the performance of deep autoencoders for feature selection and
dimension reduction for facial expression recognition on multiple levels of
hidden layers. The features extracted from the stacked autoencoder outperformed
when compared to other state-of-the-art feature selection and dimension
reduction techniques
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification
Although extreme learning machine (ELM) has been successfully applied to a
number of pattern recognition problems, it fails to pro-vide sufficient good
results in hyperspectral image (HSI) classification due to two main drawbacks.
The first is due to the random weights and bias of ELM, which may lead to
ill-posed problems. The second is the lack of spatial information for
classification. To tackle these two problems, in this paper, we propose a new
framework for ELM based spectral-spatial classification of HSI, where
probabilistic modelling with sparse representation and weighted composite
features (WCF) are employed respectively to derive the op-timized output
weights and extract spatial features. First, the ELM is represented as a
concave logarithmic likelihood function under statistical modelling using the
maximum a posteriori (MAP). Second, the sparse representation is applied to the
Laplacian prior to effi-ciently determine a logarithmic posterior with a unique
maximum in order to solve the ill-posed problem of ELM. The variable splitting
and the augmented Lagrangian are subsequently used to further reduce the
computation complexity of the proposed algorithm and it has been proven a more
efficient method for speed improvement. Third, the spatial information is
extracted using the weighted compo-site features (WCFs) to construct the
spectral-spatial classification framework. In addition, the lower bound of the
proposed method is derived by a rigorous mathematical proof. Experimental
results on two publicly available HSI data sets demonstrate that the proposed
methodology outperforms ELM and a number of state-of-the-art approaches.Comment: 16 pages, 6 figuers and 4 table
Authentication of Amadeo de Souza-Cardoso Paintings and Drawings With Deep Learning
Art forgery has a long-standing history that can be traced back to the Roman period and
has become more rampant as the art market continues prospering. Reports disclosed that
uncountable artworks circulating on the art market could be fake. Even some principal
art museums and galleries could be exhibiting a good percentage of fake artworks. It
is therefore substantially important to conserve cultural heritage, safeguard the interest
of both the art market and the artists, as well as the integrity of artists’ legacies. As a
result, art authentication has been one of the most researched and well-documented fields
due to the ever-growing commercial art market in the past decades. Over the past years,
the employment of computer science in the art world has flourished as it continues to
stimulate interest in both the art world and the artificial intelligence arena. In particular, the
implementation of Artificial Intelligence, namely Deep Learning algorithms and Neural
Networks, has proved to be of significance for specialised image analysis. This research
encompassed multidisciplinary studies on chemistry, physics, art and computer science.
More specifically, the work presents a solution to the problem of authentication of heritage
artwork by Amadeo de Souza-Cardoso, namely paintings, through the use of artificial
intelligence algorithms. First, an authenticity estimation is obtained based on processing of
images through a deep learning model that analyses the brushstroke features of a painting.
Iterative, multi-scale analysis of the images is used to cover the entire painting and produce
an overall indication of authenticity. Second, a mixed input, deep learning model is
proposed to analyse pigments in a painting. This solves the image colour segmentation
and pigment classification problem using hyperspectral imagery. The result is used to
provide an indication of authenticity based on pigment classification and correlation with
chemical data obtained via XRF analysis. Further algorithms developed include a deep
learning model that tackles the pigment unmixing problem based on hyperspectral data.
Another algorithm is a deep learning model that estimates hyperspectral images from
sRGB images. Based on the established algorithms and results obtained, two applications
were developed. First, an Augmented Reality mobile application specifically for the
visualisation of pigments in the artworks by Amadeo. The mobile application targets the
general public, i.e., art enthusiasts, museum visitors, art lovers or art experts. And second, a desktop application with multiple purposes, such as the visualisation of pigments and
hyperspectral data. This application is designed for art specialists, i.e., conservators and
restorers. Due to the special circumstances of the pandemic, trials on the usage of these
applications were only performed within the Department of Conservation and Restoration
at NOVA University Lisbon, where both applications received positive feedback.A falsificação de arte tem uma história de longa data que remonta ao período romano
e tornou-se mais desenfreada à medida que o mercado de arte continua a prosperar.
Relatórios revelaram que inúmeras obras de arte que circulam no mercado de arte podem
ser falsas. Mesmo alguns dos principais museus e galerias de arte poderiam estar exibindo
uma boa porcentagem de obras de arte falsas. Por conseguinte, é extremamente importante
conservar o património cultural, salvaguardar os interesses do mercado da arte e dos artis-
tas, bem como a integridade dos legados dos artistas. Como resultado, a autenticação de
arte tem sido um dos campos mais pesquisados e bem documentados devido ao crescente
mercado de arte comercial nas últimas décadas.Nos últimos anos, o emprego da ciência
da computação no mundo da arte floresceu à medida que continua a estimular o interesse
no mundo da arte e na arena da inteligência artificial. Em particular, a implementação da
Inteligência Artificial, nomeadamente algoritmos de aprendizagem profunda (ou Deep
Learning) e Redes Neuronais, tem-se revelado importante para a análise especializada de
imagens.Esta investigação abrangeu estudos multidisciplinares em química, física, arte e
informática. Mais especificamente, o trabalho apresenta uma solução para o problema da
autenticação de obras de arte patrimoniais de Amadeo de Souza-Cardoso, nomeadamente
pinturas, através da utilização de algoritmos de inteligência artificial. Primeiro, uma esti-
mativa de autenticidade é obtida com base no processamento de imagens através de um
modelo de aprendizagem profunda que analisa as características de pincelada de uma
pintura. A análise iterativa e multiescala das imagens é usada para cobrir toda a pintura e
produzir uma indicação geral de autenticidade. Em segundo lugar, um modelo misto de
entrada e aprendizagem profunda é proposto para analisar pigmentos em uma pintura.
Isso resolve o problema de segmentação de cores de imagem e classificação de pigmentos
usando imagens hiperespectrais. O resultado é usado para fornecer uma indicação de
autenticidade com base na classificação do pigmento e correlação com dados químicos
obtidos através da análise XRF. Outros algoritmos desenvolvidos incluem um modelo
de aprendizagem profunda que aborda o problema da desmistura de pigmentos com
base em dados hiperespectrais. Outro algoritmo é um modelo de aprendizagem profunda
estabelecidos e nos resultados obtidos, foram desenvolvidas duas aplicações. Primeiro,
uma aplicação móvel de Realidade Aumentada especificamente para a visualização de
pigmentos nas obras de Amadeo. A aplicação móvel destina-se ao público em geral, ou
seja, entusiastas da arte, visitantes de museus, amantes da arte ou especialistas em arte.
E, em segundo lugar, uma aplicação de ambiente de trabalho com múltiplas finalidades,
como a visualização de pigmentos e dados hiperespectrais. Esta aplicação é projetada para
especialistas em arte, ou seja, conservadores e restauradores. Devido às circunstâncias
especiais da pandemia, os ensaios sobre a utilização destas aplicações só foram realizados
no âmbito do Departamento de Conservação e Restauro da Universidade NOVA de Lisboa,
onde ambas as candidaturas receberam feedback positivo
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data,
the conventional supervised learning can not ensure the discrimination of the
learned feature for hyperspectral image (HSI) classification. In this paper, we
propose a modified diversity of class probability estimation (MDCPE) with two
deep neural networks to learn spectral-spatial feature for HSI classification.
In co-training phase, recurrent neural network (RNN) and convolutional neural
network (CNN) are utilized as two learners to extract features from labeled and
unlabeled data. Based on the extracted features, MDCPE selects most credible
samples to update initial labeled data by combining k-means clustering with the
traditional diversity of class probability estimation (DCPE) co-training. In
this way, MDCPE can keep new labeled data class-balanced and extract
discriminative features for both the minority and majority classes. During
testing process, classification results are acquired by co-decision of the two
learners. Experimental results demonstrate that the proposed semi-supervised
co-training method can make full use of unlabeled information to enhance
generality of the learners and achieve favorable accuracies on all three widely
used data sets: Salinas, Pavia University and Pavia Center.Comment: 13 pages, 10 figures and 8 table
Going Deeper with Contextual CNN for Hyperspectral Image Classification
In this paper, we describe a novel deep convolutional neural network (CNN)
that is deeper and wider than other existing deep networks for hyperspectral
image classification. Unlike current state-of-the-art approaches in CNN-based
hyperspectral image classification, the proposed network, called contextual
deep CNN, can optimally explore local contextual interactions by jointly
exploiting local spatio-spectral relationships of neighboring individual pixel
vectors. The joint exploitation of the spatio-spectral information is achieved
by a multi-scale convolutional filter bank used as an initial component of the
proposed CNN pipeline. The initial spatial and spectral feature maps obtained
from the multi-scale filter bank are then combined together to form a joint
spatio-spectral feature map. The joint feature map representing rich spectral
and spatial properties of the hyperspectral image is then fed through a fully
convolutional network that eventually predicts the corresponding label of each
pixel vector. The proposed approach is tested on three benchmark datasets: the
Indian Pines dataset, the Salinas dataset and the University of Pavia dataset.
Performance comparison shows enhanced classification performance of the
proposed approach over the current state-of-the-art on the three datasets.Comment: 14 page
Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification
Hyperspectral image (HSI) classification plays a significant in the field of
remote sensing due to its ability to provide spatial and spectral information.
Due to the rapid development and increasing of hyperspectral remote sensing
technology, many methods have been developed for HSI classification but still a
lack of achieving the better performance. A Frost Filtered Scale-Invariant
Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC)
technique is introduced for classifying the hyperspectral image with higher
accuracy and minimum time consumption. The FFSIFT-MLPC technique performs three
major processes, namely preprocessing, feature extraction and classification
using multiple layers. Initially, the hyperspectral image is divided into
number of spectral bands. These bands are given as input in the input layer of
perceptron. Then the Frost filter is used in FFSIFT-MLPC technique for
preprocessing the input bands which helps to remove the noise from
hyper-spectral image at the first hidden layer. After preprocessing task,
texture, color and object features of hyper-spectral image are extracted at
second hidden layer using Gaussian distributive scale-invariant feature
transform. At the third hidden layer, Euclidean distance is measured between
the extracted features and testing features. Finally, feature matching is
carried out at the output layer for hyper-spectral image classification. The
classified outputs are resulted in terms of spectral bands (i.e., different
colors). Experimental analysis is performed with PSNR, classification accuracy,
false positive rate and classification time with number of spectral bands. The
results evident that presented FFSIFT-MLPC technique improves the hyperspectral
image classification accuracy, PSNR and minimizes false positive rate as well
as classification time than the state-of-the-art methods
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