Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution

Roy, Swalpa Kumar; Kar, Purbayan; Hong, Danfeng; Wu, X.; Chanussot, Jocelyn

doi:10.1109/tgrs.2021.3120198

Cited by 32 publications

(12 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep-learning methods can automatically extract high-level spatial and spectral features simultaneously [50]. This advantage of deep-learning methods means that they have been used for many applications in RS, such as change detection [51], classification [52], anomaly detection [53], and damage mapping [54].…”

Section: Deep-feature Extractionmentioning

confidence: 99%

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021

Self Cite

View full text Add to dashboard Cite

Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

show abstract

Section: Deep-feature Extractionmentioning

confidence: 99%

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the success of deep learning-based networks in machine learning and computer vision applications [38], [39], recently, a variety of deep neural networks has been proposed for hyperspectral unmixing ( [40]). These networks are mainly based on variations of deep encoder-decoder networks.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Unmixing Using Transformer Network

Ghosh¹,

Roy²,

Koirala

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in CNN models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, i.e., Samson, Apex, and Washington DC mall and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU.

show abstract

“…Thus rendering the use of linear transformation or feature learning methods [31] for HSIC. To overcome the non-linearity issues, Convolutional Neural Network (CNN) was proposed to extract both high as well as low-level features which ultimately lead to the extraction of abstract and invariant features [32], [33]. As a result, 2D CNN achieved remarkable performance but unfortunately not so good for HSIC due to the missing channel-related information, i.e.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Khan

Mazzara

Distefano

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixel-wise classification challenging for conventional methods. To deal with nonlinearity issues in Hyperspectral Image Classification (HSIC), Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting Attention-fused Hybrid Network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using highlevel features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models.

show abstract

Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution

Cited by 32 publications

References 71 publications

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Hyperspectral Unmixing Using Transformer Network

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Contact Info

Product

Resources

About