2017
DOI: 10.3390/ijgi6110334
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Detection of Moving Ships in Sequences of Remote Sensing Images

Abstract: High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image in the sequence vary considerably. Therefore, more requirements are placed on the target detection algorithm. Aiming at the characteristics of multi-view image sequences, we propose an approach to detect moving ships on the water surface. Based on mark… Show more

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Cited by 5 publications
(2 citation statements)
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“…Otsu's method mainly divides the image into a target and background according to the gray-level difference of the image and determines the optimal threshold by obtaining the inter-class variance σ 2 B between the ground objects. The algorithm is efficient and fast, and the execution efficiency is high [31,32]. The calculation formula is as shown in (19), where P 1 and P 2 are the probability that the image pixels are divided into the target pixels and the background pixels and m 1 and m 2 are the average gray-level values of the image pixels divided into target pixels and the background pixels.…”
Section: Ground Object Extractionmentioning
confidence: 99%
“…Otsu's method mainly divides the image into a target and background according to the gray-level difference of the image and determines the optimal threshold by obtaining the inter-class variance σ 2 B between the ground objects. The algorithm is efficient and fast, and the execution efficiency is high [31,32]. The calculation formula is as shown in (19), where P 1 and P 2 are the probability that the image pixels are divided into the target pixels and the background pixels and m 1 and m 2 are the average gray-level values of the image pixels divided into target pixels and the background pixels.…”
Section: Ground Object Extractionmentioning
confidence: 99%
“…Currently, traditional ship detection algorithms are mainly classified into background modeling [1,2], temporal difference [3,4], optical flow [5,6], and template matching [7,8]. Most of these algorithms are bottom-up and differentiate targets from background regions by designing a large number of image-related features (such as grayscale, texture, edges, etc.…”
Section: Introductionmentioning
confidence: 99%