1,460 research outputs found
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
The past years have witnessed great progress on remote sensing (RS) image
interpretation and its wide applications. With RS images becoming more
accessible than ever before, there is an increasing demand for the automatic
interpretation of these images. In this context, the benchmark datasets serve
as essential prerequisites for developing and testing intelligent
interpretation algorithms. After reviewing existing benchmark datasets in the
research community of RS image interpretation, this article discusses the
problem of how to efficiently prepare a suitable benchmark dataset for RS image
interpretation. Specifically, we first analyze the current challenges of
developing intelligent algorithms for RS image interpretation with bibliometric
investigations. We then present the general guidances on creating benchmark
datasets in efficient manners. Following the presented guidances, we also
provide an example on building RS image dataset, i.e., Million-AID, a new
large-scale benchmark dataset containing a million instances for RS image scene
classification. Several challenges and perspectives in RS image annotation are
finally discussed to facilitate the research in benchmark dataset construction.
We do hope this paper will provide the RS community an overall perspective on
constructing large-scale and practical image datasets for further research,
especially data-driven ones
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
The classification of large-scale high-resolution SAR land cover images
acquired by satellites is a challenging task, facing several difficulties such
as semantic annotation with expertise, changing data characteristics due to
varying imaging parameters or regional target area differences, and complex
scattering mechanisms being different from optical imaging. Given a large-scale
SAR land cover dataset collected from TerraSAR-X images with a hierarchical
three-level annotation of 150 categories and comprising more than 100,000
patches, three main challenges in automatically interpreting SAR images of
highly imbalanced classes, geographic diversity, and label noise are addressed.
In this letter, a deep transfer learning method is proposed based on a
similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a
top-2 smooth loss function with cost-sensitive parameters was introduced to
tackle the label noise and imbalanced classes' problems. The proposed method
shows high efficiency in transferring information from a similarly annotated
remote sensing dataset, a robust performance on highly imbalanced classes, and
is alleviating the over-fitting problem caused by label noise. What's more, the
learned deep model has a good generalization for other SAR-specific tasks, such
as MSTAR target recognition with a state-of-the-art classification accuracy of
99.46%
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works
Deep learning has achieved great success in learning features from massive
remote sensing images (RSIs). To better understand the connection between
feature learning paradigms (e.g., unsupervised feature learning (USFL),
supervised feature learning (SFL), and self-supervised feature learning
(SSFL)), this paper analyzes and compares them from the perspective of feature
learning signals, and gives a unified feature learning framework. Under this
unified framework, we analyze the advantages of SSFL over the other two
learning paradigms in RSIs understanding tasks and give a comprehensive review
of the existing SSFL work in RS, including the pre-training dataset,
self-supervised feature learning signals, and the evaluation methods. We
further analyze the effect of SSFL signals and pre-training data on the learned
features to provide insights for improving the RSI feature learning. Finally,
we briefly discuss some open problems and possible research directions.Comment: 24 pages, 11 figures, 3 table
Deep Active Learning for Computer Vision: Past and Future
As an important data selection schema, active learning emerges as the
essential component when iterating an Artificial Intelligence (AI) model. It
becomes even more critical given the dominance of deep neural network based
models, which are composed of a large number of parameters and data hungry, in
application. Despite its indispensable role for developing AI models, research
on active learning is not as intensive as other research directions. In this
paper, we present a review of active learning through deep active learning
approaches from the following perspectives: 1) technical advancements in active
learning, 2) applications of active learning in computer vision, 3) industrial
systems leveraging or with potential to leverage active learning for data
iteration, 4) current limitations and future research directions. We expect
this paper to clarify the significance of active learning in a modern AI model
manufacturing process and to bring additional research attention to active
learning. By addressing data automation challenges and coping with automated
machine learning systems, active learning will facilitate democratization of AI
technologies by boosting model production at scale.Comment: Accepted by APSIPA Transactions on Signal and Information Processin
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