90 research outputs found
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
A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model
Stack autoencoder (SAE), as a representative deep network, has unique and
excellent performance in feature learning, and has received extensive attention
from researchers. However, existing deep SAEs focus on original samples without
considering the hierarchical structural information between samples. To address
this limitation, this paper proposes a new SAE model-neighbouring envelope
embedded stack autoencoder ensemble (NE_ESAE). Firstly, the neighbouring sample
envelope learning mechanism (NSELM) is proposed for preprocessing of input of
SAE. NSELM constructs sample pairs by combining neighbouring samples. Besides,
the NSELM constructs a multilayer sample spaces by multilayer iterative mean
clustering, which considers the similar samples and generates layers of
envelope samples with hierarchical structural information. Second, an embedded
stack autoencoder (ESAE) is proposed and trained in each layer of sample space
to consider the original samples during training and in the network structure,
thereby better finding the relationship between original feature samples and
deep feature samples. Third, feature reduction and base classifiers are
conducted on the layers of envelope samples respectively, and output
classification results of every layer of samples. Finally, the classification
results of the layers of envelope sample space are fused through the ensemble
mechanism. In the experimental section, the proposed algorithm is validated
with over ten representative public datasets. The results show that our method
significantly has better performance than existing traditional feature learning
methods and the representative deep autoencoders.Comment: 17 pages,6 figure
Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life
applications because it benefits from the detailed spectral information
contained in each pixel. Notably, the complex characteristics i.e., the
nonlinear relation among the captured spectral information and the
corresponding object of HSI data make accurate classification challenging for
traditional methods. In the last few years, Deep Learning (DL) has been
substantiated as a powerful feature extractor that effectively addresses the
nonlinear problems that appeared in a number of computer vision tasks. This
prompts the deployment of DL for HSI classification (HSIC) which revealed good
performance. This survey enlists a systematic overview of DL for HSIC and
compared state-of-the-art strategies of the said topic. Primarily, we will
encapsulate the main challenges of traditional machine learning for HSIC and
then we will acquaint the superiority of DL to address these problems. This
survey breakdown the state-of-the-art DL frameworks into spectral-features,
spatial-features, and together spatial-spectral features to systematically
analyze the achievements (future research directions as well) of these
frameworks for HSIC. Moreover, we will consider the fact that DL requires a
large number of labeled training examples whereas acquiring such a number for
HSIC is challenging in terms of time and cost. Therefore, this survey discusses
some strategies to improve the generalization performance of DL strategies
which can provide some future guidelines
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends
Masked Auto-Encoding Spectral-Spatial Transformer for Hyperspectral Image Classification
Deep learning has certainly become the dominant trend in hyperspectral (HS) remote sensing (RS) image classification owing to its excellent capabilities to extract highly discriminating spectral–spatial features. In this context, transformer networks have recently shown prominent results in distinguishing even the most subtle spectral differences because of their potential to characterize sequential spectral data. Nonetheless, many complexities affecting HS remote sensing data (e.g., atmospheric effects, thermal noise, quantization noise) may severely undermine such potential since no mode of relieving noisy feature patterns has still been developed within transformer networks. To address the problem, this article presents a novel masked auto-encoding spectral–spatial transformer (MAEST), which gathers two different collaborative branches: 1) a reconstruction path, which dynamically uncovers the most robust encoding features based on a masking auto-encoding strategy, and 2) a classification path, which embeds these features onto a transformer network to classify the data focusing on the features that better reconstruct the input. Unlike other existing models, this novel design pursues to learn refined transformer features considering the aforementioned complexities of the HS remote sensing image domain. The experimental comparison, including several state-of-the-art methods and benchmark datasets, shows the superior results obtained by MAEST. The codes of this article will be available at https://github.com/ibanezfd/MAEST
Deep learning for remote sensing image classification:A survey
Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel?wise and scene?wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL?based RS methods is also provided. Finally, the challenges and potential directions for further research are discussedpublishersversionPeer reviewe
Blind Hyperspectral Unmixing Using Autoencoders
The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds.Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing)
með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning).
Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar
leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur
aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning)
og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional
neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar.
Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem
hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og
útfærslur á þeim.
í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara
spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með
sjálfkóðurum.
Megin framlag ritgerðarinnar er eftirfarandi:
- Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í
fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm
til að aðgreina grennd af rófum í einu.
- Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara
og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches)
og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina.
- Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru
kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu
sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum.The Icelandic Research Fund under Grants 174075-05 and 207233-05
- …