Naoto Yakoya

Bio: Naoto Yakoya is an academic researcher. The author has contributed to research in topics: Hyperspectral imaging. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Abstract: Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models.

A Lightweight Model of VGG-16 for Remote Sensing Image Classification

Abstract: In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing. Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%–98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, and less image classification.

UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion

Abstract: To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) of the same scene. Deep learning has been widely applied in the field of HSI-MSI fusion, which is limited with hardware. In order to break the limits, we construct an unsupervised multiattention-guided network named UMAG-Net without training data to better accomplish HSI-MSI fusion. UMAG-Net first extracts deep multiscale features of MSI by using a multiattention encoding network. Then, a loss function containing a pair of HSI and MSI is used to iteratively update parameters of UMAG-Net and learn prior knowledge of the fused image. Finally, a multiscale feature-guided network is constructed to generate an HR-HSI. The experimental results show the visual and quantitative superiority of the proposed method compared to other methods.

A Multiscale Dual-Branch Feature Fusion and Attention Network for Hyperspectral Images Classification

Abstract: Recently, hyperspectral image classification based on deep learning has achieved considerable attention. Many convolutional neural network classification methods have emerged and exhibited superior classification performance. However, most methods focus on extracting features by using fixed convolution kernels and layer-wise representation, resulting in feature extraction singleness. Additionally, the feature fusion process is rough and simple. Numerous methods get accustomed to fusing different levels of features by stacking modules hierarchically, which ignore the combination of shallow and deep spectral-spatial features. In order to overcome the preceding issues, a novel multiscale dual-branch feature fusion and attention network is proposed. Specifically, we design a multiscale feature extraction (MSFE) module to extract spatial-spectral features at a granular level and expand the range of receptive fields, thereby enhancing the MSFE ability. Subsequently, we develop a dual-branch feature fusion interactive module that integrates the residual connection's feature reuse property and the dense connection's feature exploration capability, obtaining more discriminative features in both spatial and spectral branches. Additionally, we introduce a novel shuffle attention mechanism that allows for adaptive weighting of spatial and spectral features, further improving classification performance. Experimental results on three benchmark datasets demonstrate that our model outperforms other state-of-the-art methods while incurring the lower computational cost.

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

Abstract: 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.