Hyperspectral Image Classification Using Deep Pixel-Pair Features

Li, Wei; Wu, Guodong; Zhang, Fan; Du, Qian

doi:10.1109/tgrs.2016.2616355

Cited by 704 publications

(364 citation statements)

References 28 publications

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“…Santara et al [29] propose an end-to-end band-adaptive spectral-spatial feature learning network to address the problems of the curse of dimensionality. In [30], to allow CNN appropriately trained using limited labeled data, authors present a novel pixel-pair CNN to significantly augment the number of training samples.…”

Section: A Hyperspectral Image Analysismentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,346

1,204

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.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. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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Section: A Hyperspectral Image Analysismentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,346

1,204

View full text Add to dashboard Cite

show abstract

“…In addition, 1-D CNN [55]- [58], 1-D GAN [46], [59], and RNN [44], [58], [60] were also used to extract spectral features for HSI classification. In [61], Li et al used pixel-pair features extracted by CNN to explore correlation between hyperpsectral pixels, where the convolution operation was mainly executed in the spectral domain. Furthermore, in [62], [63], the training of a deep network with the dictionary learning was reformulated.…”

Section: A Spectral-feature Networkmentioning

confidence: 99%

Deep Learning for Hyperspectral Image Classification: An Overview

Li¹,

Song²,

Fang³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

1,220

452

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Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectralfeature networks, spatial-feature networks, and spectral-spatialfeature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

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“…Semantic segmentation in HSI is often treated as a pixel classification problem due to a lack of sufficient samples. Most approaches fall under three categories: (1) spectral classifiers, (2) spatial classifiers and (3) as a Siamese network problem [14]. Hao et al designed a twostream architecture, where stream1 used a stacked denoising autoencoder to encode the spectral values of each input pixel of a patch and stream2 used a CNN to process the patch's spatial features [15].…”

Section: B Semantic Segmentationmentioning

confidence: 99%

AeroRIT: A New Scene for Hyperspectral Image Analysis

Rangnekar

Mokashi

Ientilucci

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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We investigate modifying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRITthat is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various subcategories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the performance of three popular architectures -SegNet, U-Net, and Res-U-Net, for scene understanding and object identification via the task of dense semantic segmentation to establish a benchmark for the scene. To further strengthen the network, we add squeeze and excitation blocks for better channel interactions and use self-supervised learning for better encoder initialization. Aerial hyperspectral image analysis has been restricted to small datasets with limited train/test splits capabilities and we believe that AeroRIT will help advance the research in the field with a more complex object distribution to perform well on. The full dataset, with flight lines in radiance and reflectance domain, is available for download at https://github.com/aneesh3108/AeroRIT. This dataset is the first step towards developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks like vehicle tracking and occlusion handling.

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Hyperspectral Image Classification Using Deep Pixel-Pair Features

Cited by 704 publications

References 28 publications

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning for Hyperspectral Image Classification: An Overview

AeroRIT: A New Scene for Hyperspectral Image Analysis

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