Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image

Lu, Xiaochen; Zhang, Junping; Yang, Dezheng; Xu, Longting; Jia, Fengde

doi:10.1109/tip.2021.3098246

Cited by 23 publications

(13 citation statements)

References 43 publications

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“…More detector stages in the cascade are successively more discriminating against near false positives, making it a multistage variant of the R-CNN. The R-CNN stages are trained in a cascade, one after the other, successively [13].…”

Section: Proposed Model Working Proceduresmentioning

confidence: 99%

Convolutional Neural Network Based Partial Face Detection

Islam

Ahmed

Raihanur

et al. 2022

2022 IEEE 7th International Conference for Convergence in Technology (I2CT)

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Due to the massive explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In the world, there are a lot of scenarios where a simple crime can be prevented before it may even happen or find the person responsible for it. A face is one distinctive feature that we have and can differentiate easily among many other species. But not just different species, it also plays a significant role in determining someone from the same species as us, humans. Regarding this critical feature, a single problem occurs most often nowadays. When the camera is pointed, it cannot detect a person's face, and it becomes a poor image. On the other hand, where there was a robbery and a security camera installed, the robber's identity is almost indistinguishable due to the low-quality camera. But just making an excellent algorithm to work and detecting a face reduces the cost of hardware, and it doesn't cost that much to focus on that area. Facial recognition, widget control, and such can be done by detecting the face correctly. This study aims to create and enhance a machine learning model that correctly recognizes faces. Total 627 Data have been collected from different Bangladeshi people's faces on four angels. In this work, CNN, Harr Cascade, Cascaded CNN, Deep CNN & MTCNN are these five machine learning approaches implemented to get the best accuracy of our dataset. After creating and running the model, Multi-Task Convolutional Neural Network (MTCNN) achieved 96.2% best model accuracy with training data rather than other machine learning models.

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Section: Proposed Model Working Proceduresmentioning

confidence: 99%

Convolutional Neural Network Based Partial Face Detection

Islam

Ahmed

Raihanur

et al. 2022

2022 IEEE 7th International Conference for Convergence in Technology (I2CT)

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“…in which η is the balance parameter that controls the approximation and the sparsity of abundances. In this work, we use || || 2 instead of || || 0 to promote the generalization ability of the network [44,52]. Note that the SSTM defined in (6) S ∈ R p 2 ×p 2 is actually a local transform matrix with p 2 × p 2 elements, which transforms each pixel from the HR image patch to the LR patch, rather than the global transform matrix in (3).…”

Section: Hs Image Super-resolution Via Ucnnmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Yang

Zhang

et al. 2021

Remote Sensing

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Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution information of auxiliary sources. To fully exploit the spectral and spatial characteristics, in this paper, a novel single HS image SR approach is proposed based on a spatial correlation-regularized unmixing convolutional neural network (CNN). The proposed approach takes advantage of a CNN to explore the collaborative spatial and spectral information of an HS image and infer the high-resolution abundance maps, thereby reconstructing the anticipated high-resolution HS image via the linear spectral mixture model. Moreover, a dual-branch architecture network and spatial spread transform function are employed to characterize the spatial correlation between the high- and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image. Experiments on three public remote sensing HS images demonstrate the feasibility and superiority in terms of spectral fidelity, compared with some state-of-the-art HS image super-resolution methods.

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“…The 2013 IEEE GRSS Data Fusion Contest (GRSS_DFC_2013) dataset was collected by the NSF-funded Center over the University of Houston campus [40] with its false-color image shown in Fig. 4(a).…”

Section: A Datasets Descriptionmentioning

confidence: 99%

“…The University of Pavia (UP) dataset was acquired by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor over the campus of PU located in northern Italy [40], and its false-color image is shown in Fig. 7(a).…”

Section: A Datasets Descriptionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Multilevel Joint Feature Extraction Network

Lu¹,

Yang²,

Jia³

et al. 2021

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

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Over the past few years, convolutional neural network (CNN) has been broadly adopted in remote sensing (RS) imagery processing areas due to its impressive capabilities in feature extraction. Nevertheless, it is still a challenge for CNN-based hyperspectral image (HSI) classification methods to extract more effective spectral-spatial features considering all spectral bands. Driven by this issue, we propose a novel approach to cope with the HSI classification task, referring to the multi-level joint feature extraction network (MJFEN). The proposed network makes full use of the information on each channel of HSI and transforms it into valid channel-wised spatial features through a designed convolution process. Moreover, these feature maps form global attention details to guide the extraction of spectral-spatial features, which are taken to the next level for further feature mining. Then, the features obtained at different levels are integrated for ground object classification. In contrast with several state-of-the-art HSI classification methods on four public datasets, experimental results demonstrate the effectiveness and remarkable feature extraction capability of our proposed approach.

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Cascaded Convolutional Neural Network-Based Hyperspectral Image Resolution Enhancement via an Auxiliary Panchromatic Image

Cited by 23 publications

References 43 publications

Convolutional Neural Network Based Partial Face Detection

Convolutional Neural Network Based Partial Face Detection

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Hyperspectral Image Classification Based on Multilevel Joint Feature Extraction Network

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