Large-format, information-dense satellite-based remote sensing pictures are in conflict with the restricted bandwidth for return transmission to the ground, necessitating the development of appropriate remote sensing image compression techniques. With regard to the data transmission requirements of large-format remote sensing photos, we investigate common lossless compression techniques in this study and suggest a superior approach based on ANS entropy coding. This algorithm, which is superior to Golomb entropy coding in JPEG-LS image compression algorithm in terms of simplicity, high coding efficiency, and extremely low post-compression coding redundancy, can be used to process remote sensing image entropy coding and significantly boost the performance of remote sensing image compression. Research demonstrates that when compared to the original coding scheme, the approach increases the compression ratio and efficiency, decreases the amount of data that must be downlinked, and increases transmission speed. The requirements for real-time processing of large-format remote sensing images can be met by compression ratios that are very close to the limit of lossless compression for remote sensing satellite images with less information and uniform pixel value distribution. These images also have a lower algorithm complexity and require less computing time.
Fixed-point attack on the key parts of small aerial vehicles is an important means of UAV (Unmanned Aerial Vehicle) countermeasure. Because of the fast speed and flexible attitude of fixed-wing aircraft, the detection accuracy of key points of fixed-wing aircraft in infrared images is low and the speed is slow. This paper presents an improved detection and tracking algorithm based on SVM. Firstly, the detection module extracts the fixed-wing aircraft area by image segmentation, then extracts the characteristics of the fixed-wing aircraft, then uses SVM to judge the flight direction of the fixed-wing aircraft, and then locates the key points according to the direction. The experimental results show that the proposed detection algorithm can process 30 frames per second on the platform of DSP (TSM320C6678), and still achieve a high detection rate (<93%) with very high practical value.
Stripe is a common degradation phenomenon in remote sensing images. The variation-based de-striping method, due to the defect of the model itself, always has an unnecessary influence on the stripe-free area while correcting the stripe, and cannot satisfy some requirements in high-precision quantitative applications or sensitive data processing of remote sensing images. This paper proposes a high-precision stripe correction method, which first detects the position of the stripes, and then uses the interpolation idea to correct the stripe to solve the fidelity problem of the stripe-free area in the de-striping process. We use the rational assumption that the derivative of the real signal in the stripe region (to be repaired) is consistent with the derivative of the observed signal, and then selects cubic Hermite spline interpolation method for de-striping, which can uses the derivative information of the region to be repaired (ie, the derivative information of the stripe region) to overcoming the difficulty of the existing interpolation de-stripe method not being able to work well when the stripes is too wide. The experimental results show that our method can effectively remove the stripes and maintain the stripe-free area intact.
The extraction of Region of Interest (ROI) is an important information guarantee in the application of imaging matching guidance, which directly affects the acquisition probability and matching accuracy of the target. Image segmentation is an important method to extract the Region of Interest of the target. Based on image segmentation algorithm, histogram equalization and morphological filtering, this paper proposes an effective image processing method to extract the Region of Interest of the target. (1) A variety of image threshold segmentation methods are applied to the actual processing flow, and their segmentation performance is compared and analyzed. Some image segmentation methods are obtained, which are suitable for target region extraction in template image preparation and target potential region location in matching recognition. (2) Preliminary localization of visible remote sensing images is carried, using color information, to obtain local regions, then enhance the image using histogram equalization method, finally morphological filtering is used to remove the edge noise. (3) The Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused, and the evaluation indexes such as area constraint, similarity and contrast are filtered to obtain the target region .Tests have been done with visible image and infrared image in this paper. The result indicates that the effectiveness of the morphological filter is more obvious after histogram equalization for the original image. Besides, the Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused to get a more precise Region of Interest, thus ensuring the accuracy and timeliness of imaging matching guidance.
Attention can be interpreted as a method which allocates available computing power to the most informative part of the signal. In deep learning, attention mechanism also helps us to dig out the subtle information. In hyperspectral classification, the discrimination of some land cover types depends on the fine differences of hyperspectral, but most classification methods do not focus on the fine differences between hyperspectral categories. In this paper, a hierarchical group attention classification method is proposed to focus on the differences of categories from coarse to fine, therefore, the fine differences between categories can be obtained to achieve more accurate classification. For comparison and validation, we test the proposed approach with three other classification approaches on Salinas and Indian datasets, and the experiments demonstrate that our proposed approach can distinguish the spectral subtle differences of similar categories more accurately.
Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, most of the classification methods have the problem of insufficient expression ability to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group CNN features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the "feature recalibration" strategy is used to recalibrate the spectral group CNN features. The experiment show that the proposed approach can improve the classification accuracy of categories with subtle differences.
The pantograph-catenary interaction is a safety indicator of electric multiple unit (EMU) trains. It is important for EMU trains to monitor the contact-point of pantograph and catenary in real time. In this paper, a highly efficient contactpoint- tracking approach, called horizontal-vertical enhancement and tracking method (HEATM), is proposed to enhance infrared images which are used to detect and track contact-point. The HEATM includes the following three key components. Firstly, the HEATM separates the input infrared image into the horizontal images (HIs) layer and vertical images (VIs) layer. Secondly, the contact-wire tracking model is updated by the points from HIs while the pantographtracking model is updated by the points from VIs. Finally, the key contact-points are positioned by the tracking model and continuously analyzed to obtain robust tracking signals. The quantitative and qualitative results validate the effectiveness of the proposed scheme. Moreover, our method presents very robust and efficient for contact-point detecting and tracking at a speed of 70 fps and 97.8% average accuracy in two datasets (12000 frames), which is very beneficial for its extensive application.
Visible image, compared with SAR image and infrared image, has the advantage of high resolution, clear details, etc. So it can be selected for object extraction. Water objects play an important role in locating bridges, dams and other typical buildings. This paper presents a segmentation method for visible image based on gradient of the original image, and combined with the features of the water targets. According to the feature of water targets, gray uniform, smaller entropy, and smaller local variance, water objects can be extracted automatically and effectively by using clustering method from image segmentation result.
The process of Reference image preparation, is to extract region of interest area from a image,to get a simplified image which be used as template image for other algorithms.Because of the complex of scene,one usually need to excute a set of different algorithms to get a final image which only has the information of interest area. This paper presents a new variational model which used L0 norm for idelity item, to keep information of interested area better, while removing other redundant information. Experiments show that,this method can remove information of grediant in some special range,to handle a more general case,compared with the original L0 gradient method which can only remove low frequency information. Compared with the same variational model but using L1orL2 norm,the proposed method can well retain the original information.Those advantages is very important for making the process of reference image preparation faster and easier
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