Hyperspectral Pansharpening With Deep Priors

A Multitask Network for Joint Multispectral Pansharpening on Diverse Satellite Data

Despite the rapid advance in multispectral (MS) pansharpening, existing convolutional neural network (CNN)-based methods require training on separate CNNs for different satellite datasets. However, such a single-task learning (STL) paradigm often leads to overlooking any underlying correlations between datasets. Aiming at this challenging problem, a multitask network (MTNet) is presented to accomplish joint MS pansharpening in a unified framework for images acquired by different satellites. Particularly, the pansharpening process of each satellite is treated as a specific task, while MTNet simultaneously learns from all data obtained from these satellites following the multitask learning (MTL) paradigm. MTNet shares the generic knowledge between datasets via task-agnostic subnetwork (TASNet), utilizing task-specific subnetworks (TSSNets) to facilitate the adaptation of such knowledge to a certain satellite. To tackle the limitation of the local connectivity property of the CNN, TASNet incorporates Transformer modules to derive global information. In addition, band-aware dynamic convolutions (BDConvs) are proposed that can accommodate various ground scenes and bands by adjusting their respective receptive field (RF) size. Systematic experimental results over different datasets demonstrate that the proposed approach outperforms the existing state-of-the-art (SOTA) techniques.

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named " soft mask," which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJRTH1Oi1wm/view?usp=sharing).

Curvature Consistent Network for Microscope Chip Image Super-Resolution

Detecting hardware Trojan (HT) from a microscope chip image (MCI) is crucial for many applications, such as financial infrastructure and transport security. It takes an inordinate cost in scanning high-resolution (HR) microscope images for HT detection. It is useful when the chip image is in low-resolution (LR), which can be acquired faster and at a lower cost than its HR counterpart. However, the lost details and noises due to the electric charge effect in LR MCIs will affect the detection performance, making the problem more challenging. In this article, we address this issue by first discussing why recovering curvature information matters for HT detection and then proposing a novel MCI super-resolution (SR) method via a curvature consistent network (CCN). It consists of a homogeneous workflow and a heterogeneous workflow, where the former learns a mapping between homogeneous images, i.e., LR and HR MCIs, and the latter learns a mapping between heterogeneous images, i.e., MCIs and curvature images. Besides, a collaborative fusion strategy is used to leverage features learned from both workflows level-by-level by recovering the HR image eventually. To mitigate the issue of lacking an MCI dataset, we construct a new benchmark consisting of realistic MCIs at different resolutions, called MCI. Experiments on MCI demonstrate that the proposed CCN outperforms representative SR methods by recovering more delicate circuit lines and yields higher HT detection performance. The dataset is available at github.com/RuiZhang97/CCN.

Zero-Shot Hyperspectral Sharpening

Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial resolution has become an effective way to sharpen HSIs. Recently, deep convolutional neural networks (CNNs) have achieved promising fusion performance. However, these methods often suffer from the lack of training data and limited generalization ability. To address the above problems, we present a zero-shot learning (ZSL) method for HSI sharpening. Specifically, we first propose a novel method to quantitatively estimate the spectral and spatial responses of imaging sensors with high accuracy. In the training procedure, we spatially subsample the MSI and HSI based on the estimated spatial response and use the downsampled HSI and MSI to infer the original HSI. In this way, we can not only exploit the inherent information in the HSI and MSI, but the trained CNN can also be well generalized to the test data. In addition, we take the dimension reduction on the HSI, which reduces the model size and storage usage without sacrificing fusion accuracy. Furthermore, we design an imaging model-based loss function for CNN, which further boosts the fusion performance. The experimental results show the significantly high efficiency and accuracy of our approach.

Filter Pruning via Learned Representation Median in the Frequency Domain

In this article, we propose a novel filter pruning method for deep learning networks by calculating the learned representation median (RM) in frequency domain (LRMF). In contrast to the existing filter pruning methods that remove relatively unimportant filters in the spatial domain, our newly proposed approach emphasizes the removal of absolutely unimportant filters in the frequency domain. Through extensive experiments, we observed that the criterion for "relative unimportance" cannot be generalized well and that the discrete cosine transform (DCT) domain can eliminate redundancy and emphasize low-frequency representation, which is consistent with the human visual system. Based on these important observations, our LRMF calculates the learned RM in the frequency domain and removes its corresponding filter, since it is absolutely unimportant at each layer. Thanks to this, the time-consuming fine-tuning process is not required in LRMF. The results show that LRMF outperforms state-of-the-art pruning methods. For example, with ResNet110 on CIFAR-10, it achieves a 52.3% FLOPs reduction with an improvement of 0.04% in Top-1 accuracy. With VGG16 on CIFAR-100, it reduces FLOPs by 35.9% while increasing accuracy by 0.5%. On ImageNet, ResNet18 and ResNet50 are accelerated by 53.3% and 52.7% with only 1.76% and 0.8% accuracy loss, respectively. The code is based on PyTorch and is available at https://github.com/zhangxin-xd/LRMF.

Generative Dual-Adversarial Network With Spectral Fidelity and Spatial Enhancement for Hyperspectral Pansharpening

Hyperspectral (HS) pansharpening is of great importance in improving the spatial resolution of HS images for remote sensing tasks. HS image comprises abundant spectral contents, whereas panchromatic (PAN) image provides spatial information. HS pansharpening constitutes the possibility for providing the pansharpened image with both high spatial and spectral resolution. This article develops a specific pansharpening framework based on a generative dual-adversarial network (called PS-GDANet). Specifically, the pansharpening problem is formulated as a dual task that can be solved by a generative adversarial network (GAN) with two discriminators. The spatial discriminator forces the intensity component of the pansharpened image to be as consistent as possible with the PAN image, and the spectral discriminator helps to preserve spectral information of the original HS image. Instead of designing a deep network, PS-GDANet extends GANs to two discriminators and provides a high-resolution pansharpened image in a fraction of iterations. The experimental results demonstrate that PS-GDANet outperforms several widely accepted state-of-the-art pansharpening methods in terms of qualitative and quantitative assessment.

Learning Spectral Cues for Multispectral and Panchromatic Image Fusion

Recently, deep learning based multispectral (MS) and panchromatic (PAN) image fusion methods have been proposed, which extracted features automatically and hierarchically by a series of non-linear transformations to model the complicated imaging discrepancy. But they always pay more attention to the extraction and compensation of spatial details and use the mean squared error or mean absolute error as a loss function, regardless of the preservation of spectral information contained in multispectral images. For the sake of the improvements in both spatial and spectral resolution, this paper presents a novel fusion model that takes the spectral preservation into consideration, and learns the spectral cues from the process of generating a spectrally refined multispectral image, which is constrained by a spectral loss between the generated image and the reference image. Then these spectral cues are used to modulate the PAN features to obtain final fusion result. Experimental results on reduced-resolution and full-resolution datasets demonstrate that the proposed method can obtain a better fusion result in terms of visual inspection and evaluation indices when compared with current state-of-the-art methods.

Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution

Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this article proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and the reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of data sets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method, and also, the classification results on the remote sensing data set verified the validity of the information enhanced by the proposed method.

Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain

Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spectral domain. A novel instrument concept is proposed in this study, by introducing a second PAN channel in the SWIR II (2.0–2.5 μm) spectral domain. Two extended fusion methods are proposed to process both PAN channels, namely, Gain-2P and CONDOR-2P: the first one is an extended version of the Brovey transform, whereas the second one adds mixed pixel preprocessing steps to Gain-2P. By following an exhaustive performance-assessment protocol including global, refined, and local numerical analyses supplemented by supervised classification, we evaluated the updated methods on peri-urban and urban datasets. The results confirm the significant contribution of the second PAN channel (up to 45% of improvement for both datasets with the mean normalised gap in the reflective domain and 60% in the SWIR domain only) and reveal a clear advantage for CONDOR-2P (as compared with Gain-2P) regarding the peri-urban dataset.

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

Owing to the limits of incident energy and hardware system, hyperspectral (HS) images always suffer from low spatial resolution, compared with multispectral (MS) or panchromatic (PAN) images. Therefore, image fusion has emerged as a useful technology that is able to combine the characteristics of high spectral and spatial resolutions of HS and PAN/MS images. In this paper, a novel HS and PAN image fusion method based on convolutional neural network (CNN) is proposed. The proposed method incorporates the ideas of both hyper-sharpening and MS pan-sharpening techniques, thereby employing a two-stage cascaded CNN to reconstruct the anticipated high-resolution HS image. Technically, the proposed CNN architecture consists of two sub-networks, the detail injection sub-network and unmixing sub-network. The former aims at producing a latent high-resolution MS image, whereas the latter estimates the desired high-resolution abundance maps by exploring the spatial and spectral information of both HS and MS images. Moreover, two model-training fashions are presented in this paper for the sake of effectively training our network. Experiments on simulated and real remote sensing data demonstrate that the proposed method can improve the spatial resolution and spectral fidelity of HS image, and achieve better performance than some state-of-the-art HS pan-sharpening algorithms.

Model-Guided Deep Hyperspectral Image Super-Resolution

The trade-off between spatial and spectral resolution is one of the fundamental issues in hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution hyperspectral images (HR-HSI), a compromised solution is to fuse a pair of images: one has high-resolution (HR) in the spatial domain but low-resolution (LR) in spectral-domain and the other vice versa. Model-based image fusion methods including pan-sharpening aim at reconstructing HR-HSI by solving manually designed objective functions. However, such hand-crafted prior often leads to inevitable performance degradation due to a lack of end-to-end optimization. Although several deep learning-based methods have been proposed for hyperspectral pan-sharpening, HR-HSI related domain knowledge has not been fully exploited, leaving room for further improvement. In this paper, we propose an iterative Hyperspectral Image Super-Resolution (HSISR) algorithm based on a deep HSI denoiser to leverage both domain knowledge likelihood and deep image prior. By taking the observation matrix of HSI into account during the end-to-end optimization, we show how to unfold an iterative HSISR algorithm into a novel model-guided deep convolutional network (MoG-DCN). The representation of the observation matrix by subnetworks also allows the unfolded deep HSISR network to work with different HSI situations, which enhances the flexibility of MoG-DCN. Extensive experimental results are reported to demonstrate that the proposed MoG-DCN outperforms several leading HSISR methods in terms of both implementation cost and visual quality. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/MoG-DCN.htm.

Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping

Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.