86 research outputs found
SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery
Abstract With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying "from-to" change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method
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
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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
- …