CN109086701A - A kind of light fishing boat automatic identifying method for noctilucence remotely-sensed data - Google Patents
A kind of light fishing boat automatic identifying method for noctilucence remotely-sensed data Download PDFInfo
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- CN109086701A CN109086701A CN201810808844.7A CN201810808844A CN109086701A CN 109086701 A CN109086701 A CN 109086701A CN 201810808844 A CN201810808844 A CN 201810808844A CN 109086701 A CN109086701 A CN 109086701A
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Abstract
The present invention relates to a kind of light fishing boat automatic identifying methods for NPP noctilucence remotely-sensed data, comprising the following steps: screening noctilucence remotely-sensed data；The noctilucence remotely-sensed data filtered out is pre-processed, and cuts out the sub-district image of light fishing boat surrounding waters；Center Maximum Approach is taken to seek threshold value sub-district image, according to the Threshold segmentation image sought, the point greater than threshold value is retained, and the target sought at this time is suspected target image；Connection zone marker is carried out to suspected target image, the maximum value in each region and the pixel quantity in each region is counted, counts less than erosion operator target area, and record the maximum of points of target area；Erosion operation is carried out to image；Maximum value filtering is carried out to image；The image of maximum value filtering is subtracted into suspected target image, zero point is selected to export as target point.The automatization level of light fishing boat business monitoring can be improved in the present invention.
Description
Technical field
The present invention relates to fishing boat business monitoring technical fields, more particularly to a kind of light fishing for noctilucence remotely-sensed data
Ship automatic identifying method.
Background technique
The operation fishing ground of deep-sea fishing includes North Pacific, the sea areas such as southeast Pacific, South-west Atlantic, in fishing
Based on layer fish and squid, generally caught in such a way that light fishing boat night traps.How it is fast automatic obtain some
The information such as quantity, nationality, the light type of fishing zone fishing boat assess the division managements such as fisheries management the fishing boat number in each fishing zone
Amount, fishing effort, calculating fishing costs and benefits etc. has application demand.
The light fishing boat of traditional noctilucence remotely-sensed data detects, after removing noise using the methods of median filtering, according to figure
As the luminance difference of light fishing boat and background, artificial selection threshold value carries out the identification of light fishing boat.The problem is that noctilucence is distant
It is big to feel data volume, a scape image about 1G, and different phase remote sensing images are larger by noise jammings such as clouds and mists, if all taken
Manual artificial selection threshold value rule inefficiency, and the subjective experience of different mapping people influences the reliability of threshold value selection.It is existing
Some is unable to satisfy deep-sea fishing based on the light fishing boat detection method of noctilucence remotely-sensed data and answers what light fishing boat monitored automatically
Use demand.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of light fishing boat automatic identifications for noctilucence remotely-sensed data
Method improves the automatization level of light fishing boat business monitoring.
The technical solution adopted by the present invention to solve the technical problems is: providing a kind of light for noctilucence remotely-sensed data
Fishing boat automatic identifying method, comprising the following steps:
(1) noctilucence remotely-sensed data is screened；
(2) the noctilucence remotely-sensed data filtered out is pre-processed, and cuts out the sub-district figure of light fishing boat surrounding waters
Picture；
(3) center Maximum Approach is taken to seek threshold value sub-district image, according to the Threshold segmentation image sought, greater than threshold value
Point is retained, and the target sought at this time is suspected target image；
(4) connection zone marker is carried out to suspected target image, count each region maximum value and each region
Pixel quantity is counted less than erosion operator target area, and records the maximum of points of target area；
(5) erosion operation is carried out to image；
(6) maximum value filtering is carried out to image；
(7) image of maximum value filtering is subtracted into suspected target image, zero point is selected to export as target point.
The condition of screening noctilucence remotely-sensed data in the step (1) are as follows: the regional scope of screening requires to include light fishing boat
Movable sea area, time requirement has light fishing boat just in the phase of operation, and requires in light fishing boat activity sea area range cloud amount
Covering is less than 10%.
Pretreatment includes remote sensing images projection correction and noise remove in the step (2)；Wherein, the remote sensing images are thrown
Shadow correction uses Lambert projection；The noise remove is realized using median filtering and Wiener filtering.
The step (3) includes following sub-step:
(31) gradient of sub-district image transverse direction and longitudinal direction is solved；
(32) gradient mean value of transverse direction and longitudinal direction is calculated, then statistics is greater than the position letter of the gradient map picture point of mean value
Breath；
(33) statistical gradient is greater than the DN value of picture point after the corresponding pretreatment of point of gradient mean value, looks into these DN values
Maximum value and minimum value are looked for, and is maximized the average value with minimum value；
(34) threshold value for comparing both direction, is minimized；If minimum value is greater than the corresponding DN value in 98% position, protect
Stay the minimum value；If minimum value DN value corresponding less than 98%, takes 99% corresponding DN value as threshold value.Beneficial effect
Due to the adoption of the above technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit: the present invention uses the very big value filtering of adaptive extreme value Research on threshold selection and center, for noctilucence remote sensing images light fishing boat
Can automatic identification, significantly improve accuracy of identification.Applicability of the present invention is higher, noctilucence remote sensing images is applicable not only to, to it
The image of its type is also suitable, with practical value.The gradient of present invention combination image both direction and spy of target image itself
Property, the region of suspected target is calculated in original image first, maximum filter in center is then carried out in suspected target region
Wave.Do so erroneous detection caused by reducing detection of other noises to fishing boat point to a certain extent.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is NPP original graph；
Fig. 3 is pretreated subgraph；
Fig. 4 is suspected target figure；
Fig. 5 is fishing boat information identification figure.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Embodiments of the present invention are related to a kind of light fishing boat automatic identifying method for noctilucence remotely-sensed data, such as Fig. 1
It is shown, comprising the following steps: screening noctilucence remotely-sensed data；The noctilucence remotely-sensed data filtered out is pre-processed, and is cut out
The sub-district image of light fishing boat surrounding waters；Center Maximum Approach is taken to seek threshold value sub-district image, according to the threshold value sought point
Image is cut, the point greater than threshold value is retained, and the target sought at this time is suspected target image；Suspected target image is carried out
Connection zone marker counts the maximum value in each region and the pixel quantity in each region, counts less than erosion operator mesh
Region is marked, and records the maximum of points of target area；Erosion operation is carried out to image；Maximum value filtering is carried out to image；It will most
The image of big value filtering subtracts suspected target image, and zero point is selected to export as target point.
It can be seen that the present invention pre-processes noctilucence remote sensing images, it is then based on gradient image and obtains gradient mean value,
The maximum and minimum for being greater than the gradient image of gradient mean value are calculated, taking the DN value of the position greater than 99% is light fishing boat
Threshold value, compares the threshold value of both direction, retain wherein smaller value as final threshold value.The ladder of this method combination image both direction
The self character of degree and target image, calculates the region of suspected target, then in suspected target area in original image first
The very big value filtering in carry out center in domain is done so caused by reducing detection of other noises to fishing boat point to a certain extent accidentally
Inspection.
The present invention is further illustrated below by a specific embodiment.
Noctilucence remotely-sensed data is screened first, and regional scope requires to include light fishing boat activity sea area, and time requirement has light
Fishing boat requires to cover in light fishing boat activity sea area range cloud amount less than 10% just in the phase of operation.The present embodiment sieve
The noctilucence remotely-sensed data of choosing is as follows: image phase is the 21:31:12.8 in 1 day March in 2015 of UTC time, and image size is 4064
× 3072, research area is located at 56 ° of E-60 ° of E, the Indian Ocean sea area of 12 ° of N-16 ° of N.
Original NPP data are the data without projection, obtain raw video (see Fig. 2) and need to throw image later
Shadow.Firstly, raw video is projected as to wait longitudes and latitudes image using point chasing method is thrown.In order to preferably utilize the fishing boat point detected
Data wait longitudes and latitudes projection to be unable to meet demand.Due to research area be mid low latitude region, will etc. longitudes and latitudes data again into
Projection transform is gone, final data is projected as Lambert projection.It is cut out according to research area's longitude and latitude by area is studied.Subregion
In there are many noises, background inhibition is carried out to original image using median filtering and Wiener filtering.Image is such as after pretreatment
Shown in Fig. 3.
Transverse direction and longitudinal direction are calculated separately according to formula image_gradients (i)=image (i+1)-image (i)
Gradient, wherein image_gradient (i) is gradient data, and image (i) indicates the DN value in image at i-th of pixel.
Statistics is greater than the location information of the point of gradient mean value, and passes throughCalculate the ladder for being greater than gradient mean value
Spend the maximum of image and the mean value of minimum, wherein max is to be greater than the point of gradient mean value in gradient image in projection image
The maximum value of respective value, min are the minimum value for being greater than point respective value in projection image of gradient mean value in gradient image.
The threshold for comparing the solution of transverse and longitudinal both direction, selects smaller value as threshold value.In view of fishing boat point is in image
In be similar to noise spot, and be added 98% limitation.The threshold of solution DN value corresponding with 98% is compared, if
Threshold is larger, then the threshold value for taking threshold to operate as this step, otherwise chooses 99% corresponding threshold value.According to seeking
Threshold segmentation image, be retained (target indicates that background is indicated with 0 with 1), the target sought at this time greater than the point of threshold value
It is image a for suspected target image, i.e., final fishing boat point occurs in these suspected target images certainly, suspected target figure
Picture, as shown in Figure 4.
Connection zone marker carries out connection zone marker to above-mentioned doubtful image, counts the maximum of each marked region
Value and the pixel quantity in each region.It counts and is less than erosion operator target area, and record its maximum of points, for figure
As b.(preventing erosion algorithm from eroding the target for being less than erosion operator).
Maximum value filtering is carried out to image, obtains image c.The image c of maximum value filtering and suspected target image a is carried out
Subtract each other (c-a), zero point is selected to export as target point, it is image d that these points, which are exactly the fishing boat point that detected,.Most final inspection
Measuring the image come is e:e=d+b.As shown in Figure 5.
It is not difficult to find that the present invention uses the very big value filtering of adaptive extreme value Research on threshold selection and center, it is distant for noctilucence
Feel image light fishing boat can automatic identification, significantly improve accuracy of identification.
Claims (4)
1. a kind of light fishing boat automatic identifying method for noctilucence remotely-sensed data, which comprises the following steps:
(1) noctilucence remotely-sensed data is screened；
(2) the noctilucence remotely-sensed data filtered out is pre-processed, and cuts out the sub-district image of light fishing boat surrounding waters；
(3) center Maximum Approach is taken to seek threshold value sub-district image, according to the Threshold segmentation image sought, greater than the point quilt of threshold value
It remains, the target sought at this time is suspected target image；
(4) connection zone marker is carried out to suspected target image, counts the maximum value in each region and the pixel in each region
Quantity is counted less than erosion operator target area, and records the maximum of points of target area；
(5) erosion operation is carried out to image；
(6) maximum value filtering is carried out to image；
(7) image of maximum value filtering is subtracted into suspected target image, zero point is selected to export as target point.
2. the light fishing boat automatic identifying method according to claim 1 for noctilucence remotely-sensed data, which is characterized in that institute
State the condition of screening noctilucence remotely-sensed data in step (1) are as follows: the regional scope of screening requires to include light fishing boat activity sea area, when
Between require have light fishing boat just in the phase of operation, and require to be less than in the covering of light fishing boat activity sea area range cloud amount
10%.
3. the light fishing boat automatic identifying method according to claim 1 for noctilucence remotely-sensed data, which is characterized in that institute
Stating pretreatment in step (2) includes remote sensing images projection correction and noise remove；Wherein, the remote sensing images projection correction uses
Lambert projection；The noise remove is realized using median filtering and Wiener filtering.
4. the light fishing boat automatic identifying method according to claim 1 for noctilucence remotely-sensed data, which is characterized in that institute
Stating step (3) includes following sub-step:
(31) gradient of sub-district image transverse direction and longitudinal direction is solved；
(32) gradient mean value of transverse direction and longitudinal direction is calculated, then statistics is greater than the location information of the gradient map picture point of mean value；
(33) statistical gradient is greater than the DN value of picture point after the corresponding pretreatment of point of gradient mean value, searches most in these DN values
Big value and minimum value, and it is maximized the average value with minimum value；
(34) threshold value for comparing both direction, is minimized；If minimum value is greater than the corresponding DN value in 98% position, retaining should
Minimum value；If minimum value DN value corresponding less than 98%, takes 99% corresponding DN value as threshold value.
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Cited By (5)
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| CN109993087A (en) * | 2019-03-22 | 2019-07-09 | 中国海洋大学 | A method of saury fishing boat is identified using remote sensing nighttime light data |
| CN112785513A (en) * | 2020-08-25 | 2021-05-11 | 青岛经济技术开发区海尔热水器有限公司 | Self-adaptive median filtering method for filtering impulse noise |
| CN112836560A (en) * | 2020-06-01 | 2021-05-25 | 新亚优华(青岛)海洋水产科技有限公司 | Method for identifying lamplight fishing boat in Korean fishery agreement area in yellow sea based on different thresholds |
| CN115661434A (en) * | 2022-10-17 | 2023-01-31 | 中国人民解放军61540部队 | Night marine ship automatic identification method, system, electronic equipment and medium |
| CN118397096A (en) * | 2024-06-26 | 2024-07-26 | 武汉大学 | Night ship position identification method and device |
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| CN112785513A (en) * | 2020-08-25 | 2021-05-11 | 青岛经济技术开发区海尔热水器有限公司 | Self-adaptive median filtering method for filtering impulse noise |
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| CN115661434A (en) * | 2022-10-17 | 2023-01-31 | 中国人民解放军61540部队 | Night marine ship automatic identification method, system, electronic equipment and medium |
| CN118397096A (en) * | 2024-06-26 | 2024-07-26 | 武汉大学 | Night ship position identification method and device |
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