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.

Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep space exploration and Earth observations. This article proposes a weakly supervised discriminative learning with a spectral constrained generative adversarial network (GAN) for hyperspectral anomaly detection (HAD), called weaklyAD. It can enhance the discrimination between anomaly and background with background homogenization and anomaly saliency in cases where anomalous samples are limited and sensitive to the background. A novel probability-based category thresholding is first proposed to label coarse samples in preparation for weakly supervised learning. Subsequently, a discriminative reconstruction model is learned by the proposed network in a weakly supervised fashion. The proposed network has an end-to-end architecture, which not only includes an encoder, a decoder, a latent layer discriminator, and a spectral discriminator competitively but also contains a novel Kullback-Leibler (KL) divergence-based orthogonal projection divergence (OPD) spectral constraint. Finally, the well-learned network is used to reconstruct HSIs captured by the same sensor. Our work paves a new weakly supervised way for HAD, which intends to match the performance of supervised methods without the prerequisite of manually labeled data. Assessments and generalization experiments over real HSIs demonstrate the unique promise of such a proposed approach.

Anomaly detection is one of the most important applications of hyperspectral imaging technology. It is a challenging task due to the high dimensionality of hyperspectral images (HSIs), redundant information, noisy bands, and the limited capability of utilizing spatial information. In this paper, we address these problems and propose a novel anomaly detection method in HSIs. Our approach, called structure tensor and guided filter (STGF)-based strategy for anomaly detection, is based on the characteristics of HSIs. First, a novel band selection algorithm is proposed to reduce dimension, remove noisy bands, and select bands with effective information. Second, the selected bands are decomposed into two parts according to the characteristics of anomalies that are usually in a small area. Followed by this step, the backgrounds are removed through a simple differential operation for each selected band. Considering that not all of the bands provide the same contributions to anomaly detection, we then fuse the differential maps by a novel adaptive weighting method to obtain an initial detection map. Finally, GF is conducted to rectify the previous map under the condition that the neighboring pixels usually have quite strong correlations with each other. Experiments have been conducted on real-scene remote sensing HSI. Comparative analyses validate that the proposed STGF method presents superior performance in terms of detection accuracy and computational time.