17 research outputs found
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
Frugal Satellite Image Change Detection with Deep-Net Inversion
Change detection in satellite imagery seeks to find occurrences of targeted
changes in a given scene taken at different instants. This task has several
applications ranging from land-cover mapping, to anthropogenic activity
monitory as well as climate change and natural hazard damage assessment.
However, change detection is highly challenging due to the acquisition
conditions and also to the subjectivity of changes. In this paper, we devise a
novel algorithm for change detection based on active learning. The proposed
method is based on a question and answer model that probes an oracle (user)
about the relevance of changes only on a small set of critical images (referred
to as virtual exemplars), and according to oracle's responses updates deep
neural network (DNN) classifiers. The main contribution resides in a novel
adversarial model that allows learning the most representative, diverse and
uncertain virtual exemplars (as inverted preimages of the trained DNNs) that
challenge (the most) the trained DNNs, and this leads to a better re-estimate
of these networks in the subsequent iterations of active learning. Experiments
show the out-performance of our proposed deep-net inversion against the related
work.Comment: arXiv admin note: text overlap with arXiv:2212.1397
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection
Satellite image change detection aims at finding occurrences of targeted
changes in a given scene taken at different instants. This task is highly
challenging due to the acquisition conditions and also to the subjectivity of
changes. In this paper, we investigate satellite image change detection using
active learning. Our method is interactive and relies on a question and answer
model which asks the oracle (user) questions about the most informative display
(dubbed as virtual exemplars), and according to the user's responses, updates
change detections. The main contribution of our method consists in a novel
adversarial model that allows frugally probing the oracle with only the most
representative, diverse and uncertain virtual exemplars. The latter are learned
to challenge the most the trained change decision criteria which ultimately
leads to a better re-estimate of these criteria in the following iterations of
active learning. Conducted experiments show the out-performance of our proposed
adversarial display model against other display strategies as well as the
related work.Comment: arXiv admin note: substantial text overlap with arXiv:2203.1155
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
High spectral resolution imagery of the Earth's surface enables users to
monitor changes over time in fine-grained scale, playing an increasingly
important role in agriculture, defense, and emergency response. However, most
current algorithms are still confined to describing local features and fail to
incorporate a global perspective, which limits their ability to capture
interactions between global features, thus usually resulting in incomplete
change regions. In this paper, we propose a Global Multi-head INteractive
self-attention change Detection network (GlobalMind) to explore the implicit
correlation between different surface objects and variant land cover
transformations, acquiring a comprehensive understanding of the data and
accurate change detection result. Firstly, a simple but effective Global Axial
Segmentation (GAS) strategy is designed to expand the self-attention
computation along the row space or column space of hyperspectral images,
allowing the global connection with high efficiency. Secondly, with GAS, the
global spatial multi-head interactive self-attention (Global-M) module is
crafted to mine the abundant spatial-spectral feature involving potential
correlations between the ground objects from the entire rich and complex
hyperspectral space. Moreover, to acquire the accurate and complete
cross-temporal changes, we devise a global temporal interactive multi-head
self-attention (GlobalD) module which incorporates the relevance and variation
of bi-temporal spatial-spectral features, deriving the integrate potential same
kind of changes in the local and global range with the combination of GAS. We
perform extensive experiments on five mostly used hyperspectral datasets, and
our method outperforms the state-of-the-art algorithms with high accuracy and
efficiency.Comment: 14 page, 18 figure
SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection.
As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth's surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models