Accurately detecting and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remain challenging. Most of the existing solutions primarily design a complex deep neural network to learn strong feature representations for objects separated from the background, which often results in a heavy computation burden. In this paper, we propose an accurate yet fast object detection method for RSI, named SuperYOLO, which fuses multimodal data and performs high resolution (HR) object detection on multiscale objects by utilizing the assisted super resolution (SR) learning and considering both the detection accuracy and computation cost. First, we utilize a symmetric compact multimodal fusion (MF) to extract supplementary information from various data for improving small object detection in RSI. Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy. Moreover, to avoid introducing additional computation, the SR branch is discarded in the inference stage and the computation of the network model is reduced due to the LR input. Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO achieves an accuracy of 75.09% (in terms of mAP50), which is more than 10% higher than the SOTA large models such as YOLOv5l, YOLOv5x and RS designed YOLOrs. Meanwhile, the GFOLPs and parameter size of SuperYOLO are about 18x and 3.8x less than YOLOv5x. Our proposed model shows a favorable accuracy-speed trade-off compared to the state-of-art models. The code will be open sourced at https://github.com/icey-zhang/SuperYOLO.

The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it possible to capture anomalous samples [i.e., anomaly detection (AD)] that deviate from background samples. However, hyperspectral anomaly detection (HAD) faces various challenges due to high dimensionality, redundant information, and unlabeled and limited samples. To address these problems, this article proposes an unsupervised discriminative reconstruction constrained generative adversarial network for HAD (HADGAN). Our solution is mainly based on the assumption that the number of normal samples is much larger than the number of abnormal ones. The key contribution of this article is to learn a discriminative background reconstruction with anomaly targets being suppressed, which produces the initial detection image (i.e., the residual image between the original image and reconstructed image) with anomaly targets being highlighted and background samples being suppressed. To accomplish this goal, first, by using an autoencoder (AE) network and an adversarial latent discriminator, the latent feature layer learns normal background distribution and AE learns a background reconstruction as much as possible. Second, consistency enhanced representation and shrink constraints are added to the latent feature layer to ensure that anomaly samples are projected to similar positions as normal samples in the latent feature layer. Third, using an adversarial image feature corrector in the input space can guarantee the reliability of the generated samples. Finally, an energy-based spatial and distance-based spectral joint anomaly detector is applied in the residual map to generate the final detection map. Experiments conducted on several data sets over different scenes demonstrate its state-of-the-art performance.

Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress. To further enhance the performance with few labeled samples, we in this paper propose a novel FSL framework for HSIC with a class-covariance metric (CMFSL). Overall, the CMFSL learns global class representations for each training episode by interactively using training samples from the base and novel classes, and a synthesis strategy is employed on the novel classes to avoid overfitting. During the meta-training and meta-testing, the class labels are determined directly using the Mahalanobis distance measurement rather than an extra classifier. Benefiting from the task-adapted class-covariance estimations, the CMFSL can construct more flexible decision boundaries than the commonly used Euclidean metric. Additionally, a lightweight cross-scale convolutional network (LXConvNet) consisting of 3D and 2D convolutions is designed to thoroughly exploit the spectral-spatial information in the high-frequency and low-frequency scales with low computational complexity. Furthermore, we devise a spectral-prior-based refinement module (SPRM) in the initial stage of feature extraction, which cannot only force the network to emphasize the most informative bands while suppressing the useless ones, but also alleviate the effects of the domain shift between the base and novel categories to learn a collaborative embedding mapping. Extensive experiment results on four benchmark data sets demonstrate that the proposed CMFSL can outperform the state-of-the-art methods with few-shot annotated samples.

Convolutional neural networks (CNNs) have recently achieved impressive improvements on hyperspectral (HS) pansharpening. However, most of the CNN-based HS pansharpening approaches would have to first upsample the low-resolution hyperspectral image (LR-HSI) using bicubic interpolation or data-driven training strategy, which inevitably lose some details or greatly rely on the learning process. In addition, most previous methods regard the pansharpening as a black-box problem and treat diverse features equally, thus hindering the discriminative ability of CNNs. To conquer these issues, a novel HS pansharpening method using deep hyperspectral prior (DHP) and dual-attention residual network (DARN) is proposed in this article. Specifically, we first upsample the LR-HSI to the scale of the panchromatic (PAN) image through the DHP algorithm, which can better preserve spatial and spectral information without learning from large data sets. The upsampled result is then concatenated with the PAN image to form the input of the DARN, where several channel-spatial attention residual blocks (CSA ResBlocks) are stacked to map the residual HSI between the reference HSI and the upsampled HSI. In each CSA ResBlock, two complementary attention modules, i.e., channel attention and spatial attention modules, are designed to adaptively learn more informative features of spectral channels and spatial locations simultaneously, which can effectively boost the fusion accuracy. Finally, the fused HSI is obtained by the summation of the upsampled HSI and the reconstructed residual HSI. The experimental results of both simulated and real HS data sets demonstrate that the performance of our DHP-DARN method is superior over the state-of-the-art HS pansharpening approaches.

Infrared Small Target Detection (IRSTD) refers to detecting faint targets in infrared images, which has achieved notable progress with the advent of deep learning. However, the drive for improved detection accuracy has led to larger, intricate models with redundant parameters, causing storage and computation inefficiencies. In this pioneering study, we introduce the concept of utilizing network pruning to enhance the efficiency of IRSTD. Due to the challenge posed by low signal-to-noise ratios and the absence of detailed semantic information in infrared images, directly applying existing pruning techniques yields suboptimal performance. To address this, we propose a novel wavelet structure-regularized soft channel pruning method, giving rise to the efficient IRPruneDet model. Our approach involves representing the weight matrix in the wavelet domain and formulating a wavelet channel pruning strategy. We incorporate wavelet regularization to induce structural sparsity without incurring extra memory usage. Moreover, we design a soft channel reconstruction method that preserves important target information against premature pruning, thereby ensuring an optimal sparse structure while maintaining overall sparsity. Through extensive experiments on two widely-used benchmarks, our IRPruneDet method surpasses established techniques in both model complexity and accuracy. Specifically, when employing U-net as the baseline network, IRPruneDet achieves a 64.13% reduction in parameters and a 51.19% decrease in FLOPS, while improving IoU from 73.31% to 75.12% and nIoU from 70.92% to 74.30%. The code is available at https://github.com/hd0013/IRPruneDet.

Infrared small target detection (IRSTD) is important for many practical applications such as hazardous aircraft warning, especially when the target is not visible in visible light image due to atmospheric conditions such as fog and cloud. However, IRSTD is challenging due to noises, small and dim targets. To address this challenge, we propose a novel Dim2Clear Network (Dim2Clear) for IRSTD in this paper. Specifically, the Dim2Clear consists of a U-Net backbone encoder, a context mixer decoder (CMD) based on spatial and frequency attention (SFA), and an eyeball-shaped enhancement module (EEM). The CMD is composed of cascaded regular residual blocks where two SFA modules are inserted. Each SFA module receives features from different residual blocks and generates spatial attention map from them to modulate the low-level features, which are then decomposed into low and high frequencies using the discrete cosine transformation. Accordingly, features are further modulated according to the generated frequency attention maps. In this way, SFA can extract both spatial context and frequency context to improve the feature representation capacity. In addition, we design an EEM to suppress the noise and enhance the signal-to-noise ratio in the segmentation results from the perspective of image super-resolution. Experiments on the SIRST dataset and our newly constructed IRSTD-1k dataset show that the proposed Dim2Clear outperforms state-of-the-art methods.