In the context of the rapid development and widespread application of remote sensing technology, fine-grained aircraft classification is a research area with significant practical value. Most current classification algorithms, developed primarily for natural images, underperform when applied to remote sensing images. Additionally, fine-grained classification tasks inherently face challenges due to small inter-class differences and subtle discriminative features. To address these issues, this study designs an aircraft fine-grained network (AFG-Net) method, a network for fine-grained aircraft classification in remote sensing images. AFG-Net builds upon the ConvNext network, known for its superior classification capabilities over traditional CNNs and comparable to the Swin Transformer. Due to the influence of natural environment and complex imaging backgrounds on remote sensing images, this paper conducted data augmentation before training, which can help the network model better cope with interference and improve robustness. The following are the improvements made in this study: 1) Developing the ConvNext_s network, enhanced with the SimAm attention mechanism for better extraction of subtle, discriminative aircraft features. 2) Proposing a new composite loss function based on Mutual-Channel Loss, allowing the network to consider both global and local information more comprehensively, thereby improving aircraft classification performance and model robustness. 3) Demonstrating AFG-Net's applicability to fine-grained aircraft classification tasks in remote sensing images. Tested on the MTARSI and OPT-Aircraft_v1.0 datasets, AFG-Net achieves accuracies of 94.76% and 84.59%, respectively, outperforming existing advanced models in extensive experiments.
With the rapid development of remote sensing technology, remote sensing images play an important role in the agricultural field, geological field, and natural disaster detection. The size of aircraft in complex scenes in remote sensing images is extremely small, and aircraft of different models have similar shapes. Therefore, improving the accuracy of aircraft target recognition is a challenging task. We propose an improved aircraft small target recognition method based on yolov5, which can improve the recognition accuracy of aircraft targets while ensuring the speed of the model. The specific content is as follows: To address the problem of lack of remote sensing aircraft training sets, we use existing public remote sensing images to combine with aircraft model images. Most of the aircraft models only occupy a dozen to twenty pixels in the 1k*1k image, and perform Scale the generated data set; in order to better combine features of different scales and obtain higher-level feature fusion, introduce the BiFPN module with more residual connections and more complete feature fusion; use the SE attention mechanism to learn the weights of different features , extract information that is more important for detection and improve model performance; in view of the small size of the aircraft, a detection method based on Wasserstein distance such as NWD (Normalized Wasserstein Distance) is selected as the loss function.
In the context of the rapid development and widespread application of remote sensing technology, small object detection has become a prominent research focus. Despite the extensive use of the YOLOv5 network in the field of object detection, its performance in detecting small objects, especially in remote sensing images, remains unsatisfactory. Particularly, detecting and recognizing small objects, such as aircraft, pose greater challenges. The reasons for this include the small size of the targets, low contrast between targets and backgrounds, and the lack of comprehensive publicly available datasets. To address these issues, this study constructed a dataset of remote sensing images containing small aircraft targets, which facilitates the network in capturing fine-grained features and improving detection performance, thus compensating for the shortcomings of existing publicly available datasets. Based on the YOLOv5network model, this study proposed the following optimization measures: (1) To tackle the issue of small target sizes, the model structure was simplified to make the feature extraction network more suitable for small objects and to reduce the number of model parameters. (2) In response to the deficiencies in the original model's fusion method, a bidirectional Feature Pyramid Network (BiFPN) was introduced to enhance multi-level feature fusion capability. (3) To reduce the computational complexity of the model, reasonable anchor boxes were designed to enable the model to accurately focus on crucial information during the detection process. Experimental results demonstrate that the proposed algorithm improves the detection accuracy and speed of small aircraft targets in remote sensing images. On our custom dataset, the method achieves excellent results in terms of precision, computational efficiency, and parameter count.
With the rapid development of remote sensing technology, remote sensing registration plays an important role in the assessment of various natural disasters, especially earthquakes. However, multi-temporal remote sensing images for the assessment have some characteristics, e.g. large-scale and rotation, resulting in challenges of remote sensing registration. In order to better register remote sensing images, we propose a new image registration method with a deep learning feature matching strategy. We first extract the pre-match point sets M and S by using SIFT-FLANN (SIFT-Fast Library for Approximate Nearest Neighbors). Second, we filter out the correct matching point pairs from M and S by using a multiscale neighborhood information network and a dual-path ConvNeXt network with self-attention-guided local information enhancement. Thirdly, we register multi-temporal remote sensing images by solve the model parameters of the spatial transformation. Finally, we evaluate our proposed method using a variety of remote sensing images with different phases, including visible light images with different illumination, scale and geometry changes. On the remote sensing image dataset containing images of pre- and post-earthquake, we compare our method to existing state-of-the-art methods and provide the results with the evaluation indexes such as Root Mean Square Error (RMSE). The results show that our method for multi-temporal remote sensing registration has a higher registration accuracy and more robustness.
It is a challenging task to build efficient and robust model for aircraft detection. In our object recognition system, aircraft detection is a main task, which faces various problems, such as blur, occlusion, and shape variation and so on. Existing approaches always require a set of complex classification model and a large number of training samples, which is inefficient and costly. In order to deal with these problems, we employ location based informative features to reduce the complexity of training data. With the employment of location based informative features, simple classifiers will manifest high performance instead of complex classifier which requires more complicated strategy for training. Further, our system needs to update the model frequently which is similar to online learning method, in order to reducing computational complexity, a very sparse measurement matrix is applied to extract features from feature space. The construction of this sparse matrix is based on the theory of sparse representation and compressed sensing. From the experimental results, the detection rate and cost of our proposed method is better than other traditional method.
An approach is proposed to reduce the tracking jitter of the extended target in boost phase for plume tracker in a photoelectric acquisition, tracking, and pointing system. The characteristics of the vehicle imaging are analyzed and the causes of jitters are identified. The target moving direction and its principal axis are combined to calculate the optimal frontal direction. A contour smoothing method based on the chord-arc ratio filtering is introduced to obtain a preliminary extraction point with lower jitters. Then a fine tracking point extraction method based on the minimal inscribed circle of contour after filtering is presented. Experimental results confirm that the proposed method significantly improves the tracking precision and stability.
Automatic focusing (AF) is a key technology of measuring TV capturing the clear objective image in photoelectric
measurement system. It is viable to enhance the performance of measuring TV through focusing effectively and quickly.
In the process of maneuvering target tracking, the background and the feature of targets change from time to time, and
the reliability of AF is highly required. Firstly, conditions for starting AF need to be investigated. The relation between
degree of definition and edge acutance is proved by experiments. Combined with the sharpness value, it decides whether
to begin AF. Secondly, it needs focusing quickly and exactly after starting AF. The accuracy and efficiency of the
sharpness function is another key factor of AF. By comparing some favorable sharpness functions, normalized variance
and square-gradient functions are employed based on focus windows. Thirdly, the optimized mountain-climb searching
algorithm based on the defocusing extents and the adaptive searching step size is proposed. Experiments show the
algorithm proposed improves the speed and reliability of AF.
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