A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.

In the complex background, the contrast and signal-to-noise (SNR) ratio of the infrared (IR) small target are low. Therefore, the traditional IR small target detection algorithms are difficult to achieve good detection performance when the characteristics of small targets are sparse. To solve this problem, an IR small target detection network with generate label and feature mapping (GLFM)-net is proposed in this letter. First, in the GLFM-net model, a scale adaptive feature extraction network is proposed for the IR small target sparse features extraction, and then, the multilayer joint upsampling feature mapping network is proposed for small target feature mapping and background suppression. Based on this model, the feature mapping results of IR dim and small targets with the greatly suppressed background are obtained. Second, in model training, we designed a 2-D Gaussian label generation strategy for the problem of sample imbalance, which can achieve excellent detection performance by using small training samples. The experimental results show that the network can detect IR small targets with different sizes and low SNRs in various complex backgrounds and has good effectiveness and robustness compared with the existing algorithms.

In recent years, remote sensing recognition technology has found extensive applications in diverse fields, such as modern agriculture, forest management, urban planning, natural resource management, and disaster monitoring. However, the existing remote sensing recognition tasks face significant challenges because of the complex and ever-changing observation environment and the rapid growth of observation classes. The detection performance of existing closed-set recognition methods (where the test set does not contain unknown classes) is greatly limited. Therefore, numerous remote sensing open-set recognition (RSOSR) methods have been proposed to cope with more demanding but practical scenarios in the open world, including scenes or targets with unknown classes. Despite this, there is still a lack of comprehensive work on RSOSR technology. This article presents a comprehensive review of existing RSOSR technologies, covering relevant definitions, model principles, evaluation standards, and method comparisons. We then identify and discuss the limitations of current RSOSR technologies while highlighting promising research directions.

Aiming at the problems of large parameters and low detection speed of the existing remote sensing road detection network model, a lightweight remote sensing road detection network (LRSR-net) model is proposed in this letter. In the feature encoding stage of LRSR-net, an efficient road target feature extraction module dilated joint convolution module is proposed. This module can eliminate the feature loss caused by the pooling layer, so that the network model can use a small amount of parameters to learn the characteristics of road targets. In addition, in the feature decoding stage, to eliminate the damage of the target data structure caused by the upsampling operation, a joint decoding module is proposed. The joint decoding module can effectively map the characteristics of road targets and suppress the background clutter. The systematic experimental results have demonstrated that the proposed lightweight road target detection model has excellent detection performance under the condition of small model parameters, and most performance indexes are better than those of the existing advanced detection methods. The source code of this article can be obtained from https://github.com/yangzhen1252/IRSR-net .

Intractable sparse feature extraction, over-weight model size, and limited training samples are currently bewildering in infrared dim small target detection, which are not adequately addressed by current state-of-the-art (SOTA) methods. Here, to synchronously address these issues, a dense multi-level feature extraction and fusion network (DMEF-net) is designed, mainly consisting of two modules: target context and Gaussian saliency feature extraction module (TCGS) and multi-level dense feature fusion structure (MLDF). Inspired by the physical thermal diffusion model and the human visual mechanism for small-scale targets, Gaussian salience features and local context features are introduced into the target feature expression through the designed TCGS module to solve the sparse feature extraction problem. Then, to solve the over-weight model size problem, a novel MLDF module is designed to incorporate the feature reuse mechanism into our model, thereby significantly reducing the number of trainable parameters. Finally, in the training procedure, an efficient saliency labeling strategy is proposed to jointly supervise the model training in both Euclidean and Gaussian spaces, ultimately enhancing the model’s ability to explore the target features and alleviate performance deterioration when the training samples are limited. Extensive experiments on several large-scale open datasets, including TDSATUA and MTDUCB, prove that the proposed DMEF-net outperforms other SOTA methods by 8% in accuracy and has 4800% less model size.