Measurement methods

Abstract

By using synthetic aperture radar satellites, it will be possible to quickly assess the conditions of flowing water areas when necessary. [Solution] This problem is solved by a surveying method that includes an imaging step of photographing a flowing water area using spotlight mode, steering spotlight mode, or sliding spotlight mode of a synthetic aperture radar satellite; a movement trace detection step of identifying the movement traces of floating objects that flowed down the flowing water area during imaging from the images of the flowing water area captured in the imaging step; and a flow velocity calculation step of calculating the movement velocity of the floating objects from the length of the movement traces, thereby estimating the surface flow velocity of the flowing water area during the imaging step from the movement velocity of the floating objects or the average of the movement velocities of multiple floating objects.

Description

[Technical Field]

[0001] The present invention relates to a surveying technique for flowing water areas using a synthetic aperture radar satellite. [Background Art]

[0002] Earth observation technology has become an indispensable tool for understanding and managing our planet. Among such technologies, optical remote sensing has been widely used because it provides intuitive information close to human vision. An optical sensor is a passive sensor that visualizes ground conditions like a photograph by capturing the reflection of sunlight. However, this dependence on sunlight brings about a fundamental constraint of optical sensors. Not only at night, but also when the observation target is covered by clouds, fog, smoke, or bad weather, it is impossible to acquire information on the ground surface. According to statistics, approximately 75% of the Earth's surface is either in darkness or covered by clouds at any given moment, creating situations where observation by optical sensors is difficult.

[0003] This loss of observation opportunities becomes a serious problem particularly in fields where rapidity and reliability are required. For example, when a large-scale earthquake or flood occurs, immediately grasping the damage situation is extremely important for life-saving activities and the formulation of evacuation plans. However, disasters are often accompanied by bad weather, and there are many cases where optical satellites cannot obtain valid information for several days or even longer.

[0004] Synthetic Aperture Radar (SAR) has emerged as a powerful solution to this problem. SAR is an active sensor that irradiates microwaves toward the ground surface, receives the reflected waves (echoes), and generates images. Since SAR acts as its own signal source, it does not rely on sunlight and enables observation regardless of day or night. Furthermore, the microwaves used by SAR have the property of penetrating clouds, rain, fog, smoke, etc., so information on the ground surface can be acquired without being affected by weather conditions. [Prior Art Documents] [Patent Documents]

[0005] [Patent Document 1] Patent No. 7690715 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] With the increasing severity of floods due to global warming and the strengthening of disaster prevention laws and regulations, there is a need for new flood control systems and river management methods that are not inferior to existing ones. On the other hand, rivers, which are the subject of management, each have their own unique geographical and spatial characteristics, and their state changes moment by moment due to floods, making them difficult to predict. In order for rivers to maintain the safety they should have in the future, it is necessary to elucidate the mechanisms of river channel changes and the destruction of structures, and to be able to quantitatively predict the hydraulic effects of rainfall and floods.

[0007] In view of these problems, the problem that the present invention aims to solve is to enable the rapid assessment of the conditions of flowing water areas when necessary by using synthetic aperture radar satellites. [Means for solving the problem]

[0008] To solve the above problems, the present invention provides a surveying method comprising: an imaging step of photographing a flowing water area using spotlight mode, steering spotlight mode, or sliding spotlight mode of a synthetic aperture radar satellite; a movement trace detection step of identifying the movement traces of floating objects that flowed down the flowing water area during imaging from the image of the flowing water area captured in the imaging step; and a flow velocity calculation step of calculating the movement velocity of the floating objects from the length of the movement traces. The gist of the method is to estimate the surface flow velocity of the flowing water area during the imaging step from the movement velocity of the floating objects or the average of the movement velocities of multiple floating objects.

[0009] SAR (Surface-to-Aerial Radar) is a system that uses the movement of a satellite (or aircraft) to synthesize a virtual antenna with a wider aperture than the actual antenna on the aircraft, thereby obtaining high-resolution radar images. In images taken by a SAR satellite (hereinafter also referred to as "SAR images"), stationary objects are focused based on the change in Doppler frequency accompanying the satellite's movement, and point images of those objects appear at the correct position on the image. In other words, SAR images are generated under the assumption that all targets are stationary. Objects that are moving during imaging (moving objects) disrupt this assumption, causing distortion and blurring on the SAR image. Because moving objects exhibit a different change in Doppler frequency than what is expected for stationary objects, the SAR's synthetic aperture processing (azimuth compression) does not properly focus them. As a result, echoes diffuse on the image and appear as band-like images rather than points. In other words, moving objects appear as elongated trajectories (streaks) based on their direction and speed of movement. In particular, in imaging modes that achieve high resolution through long-duration illumination, such as spotlight mode, streaks of moving objects remain relatively clearly visible in the image. The movement speed of the moving object can be calculated from the relationship between the length of these streaks in the SAR image and its exposure time (seconds) and exposure direction. Applying this principle to floating objects flowing down a body of water, and considering the movement speed of the floating objects as the surface velocity of the water, the surface velocity of the water can be estimated from the SAR image.

[0010] Furthermore, in the imaging process, it is desirable for the synthetic aperture radar satellite to image the flowing water area using X-band microwaves. This is because it is possible to capture the movement traces of small floating objects such as vegetation debris and air bubbles.

[0011] Furthermore, the movement trace detection step preferably includes a step of identifying point images having different backscattering characteristics and / or intensity from the water surface as movement traces from the image of the flowing water area. The brightness of each point image in the SAR image is determined by its intensity (square of the amplitude). Also, each pixel in the SAR image has information recorded that indicates the backscattering characteristics of the target (object). By using these differences in intensity and backscattering characteristics as clues, movement traces in the image can be separated from the water surface.

[0012] In this case, it is desirable that the movement trace detection step includes a step of identifying a series of movement traces formed by the same drifting object from the backscattering characteristics of the point images constituting the movement traces.

[0013] Various types of floating debris can exist in flowing water. In particular, rivers during floods are expected to carry a large amount of natural objects such as driftwood, as well as man-made objects such as household items. Using the aforementioned backscattering characteristics as a clue, it is possible to identify to some extent the floating debris that formed each movement trace. In other words, it is possible to identify movement traces formed by the same floating debris.

[0014] Furthermore, it is desirable that the movement trace detection step includes a step of determining the direction of displacement of each movement trace in the image, taking into account the orbit of the synthetic aperture radar satellite and the flow direction in each part of the flowing water area.

[0015] Reflected waves from a moving object experience an additional frequency shift due to the object's velocity, in addition to the Doppler effect caused by the SAR satellite's movement. In particular, if the object's movement includes an approach / regression component in the range direction (the direction in which the SAR satellite emits microwaves) relative to the SAR satellite, the Doppler frequency shifts, causing the object's position in the image to shift from its original position in the azimuth direction (along the SAR satellite's orbit). More specifically, if the object is approaching the SAR satellite, its position in the image shifts towards the direction of the SAR satellite's movement (towards a higher Doppler frequency), and if it is moving away, it shifts in the opposite direction.

[0016] Here, the flow direction of each part of the observed water body can usually be easily determined from the location and shape of the water body, or it is known in advance. In other words, the direction in which a streak of drifting debris drifting down the water body deviates in the azimuth direction can be determined from its flow direction. By taking this deviation direction into account, the accuracy of the analysis of drifting debris streaks can be improved.

[0017] In this case, the movement trace detection step may include a step of joining the endpoints of the movement trace that have been divided into segments (parts). For example, if the water flow area being observed is meandering, the floating objects will also meander. If the direction of movement of the floating objects changes during filming, the position of the streak of those objects may shift, and the streak may be divided into multiple segments. In order to calculate the movement speed of the floating objects from the length of the movement trace, it is necessary to reconstruct these divided segments into a continuous movement trace.

[0018] Furthermore, in the present invention, the flow velocity calculation step may include a step of dividing the length of the movement trace by the shooting time in the shooting step. If the total length of the movement trace of the floating object can be determined, the surface flow velocity of the water flow area can be calculated by dividing that length by the shooting time.

[0019] Furthermore, in order to solve the above problems, the present invention provides a surveying method that includes: an imaging step of photographing a flowing water area with an along-track multi-channel synthetic aperture radar satellite; a movement trace detection step of identifying the movement traces of floating objects that flowed down the flowing water area during imaging from the data or images of the flowing water area acquired in the imaging step; and a flow velocity calculation step of calculating at least the range-direction velocity of the floating objects from the interference phase of the movement traces, and using that range-direction velocity as one component to calculate the movement velocity of the floating objects. The gist of the method is to estimate the surface flow velocity of the flowing water area during the imaging step from the movement velocity of the floating objects, or the average of the movement velocities of multiple floating objects.

[0020] An along-track multi-channel synthetic aperture radar satellite is a SAR satellite with multiple antennas arranged along the satellite's azimuth. These antennas observe the same point with only a very small time difference. When the target object is stationary, these return signals are in almost the same phase, but when the target object is moving, a phase difference (interference phase) occurs. By measuring this phase difference, the velocity of the moving object in the range direction can be calculated with high accuracy. Applying this principle to floating objects flowing down a body of water, and considering the velocity of the floating objects as the surface velocity of the water, the surface velocity of the water can be estimated from the SAR image.

[0021] Furthermore, in order to solve the above problem, the surveying method of the present invention comprises: an imaging step of imaging a flowing water area with a synthetic aperture radar satellite; a movement trace detection step of identifying movement traces of drifting objects that have flowed down the flowing water area during imaging from the data or image of the flowing water area obtained in the imaging step; and a flow velocity calculation step of searching for an azimuth direction velocity that maximizes the sharpness of the movement traces, and calculating the movement velocity of the drifting object with the azimuth direction velocity as one component, and the gist of the invention is that the surface flow velocity of the flowing water area during the imaging step is estimated from the movement velocity of the drifting object or the average of the movement velocities of a plurality of said drifting objects.

[0022] For example, by means of the MTR (Motion Target Refocusing) method, searching for the azimuth direction velocity at which the sharpness of the movement trace is maximized enables the azimuth direction velocity of the movement trace to be specified. By applying this principle to drifting objects flowing down a flowing water area and regarding the movement velocity of the drifting objects as the surface flow velocity of the flowing water area, the surface flow velocity of the flowing water area can be estimated from a SAR image.

Effects of the Invention

[0023] As described above, according to the surveying method of the present invention, it becomes possible to more quickly grasp the situation of a flowing water area when necessary.

Brief Description of Drawings

[0024] [Figure 1] It is a schematic diagram showing the basic functions of a SAR satellite 1. [Figure 2] It is a schematic diagram showing types of imaging modes of the SAR satellite 1. [Figure 3] It is a schematic diagram explaining how a point image appears on a SAR image of a moving body moving in the azimuth direction. [Figure 4] It is a schematic diagram explaining how a point image appears on a SAR image of a moving body moving in the range direction. [Figure 5] It is a flow diagram showing a series of procedures for estimating the surface flow velocity of a river by the surveying method of the embodiment. [Figure 6] This is a schematic diagram showing how streaks 31 appear on a SAR image when drifting debris 21 in a river is moving at a constant velocity in a straight line. [Figure 7] This is a schematic diagram showing how streaks 31 appear on a SAR image in a meandering river. [Figure 8] This is a flow diagram showing a series of procedures for estimating the surface flow velocity of a river according to another embodiment. MODE FOR CARRYING OUT THE INVENTION

[0025] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The surveying method described below is characterized in that it images a river using the spotlight mode of a SAR satellite, and estimates the surface flow velocity of the river from the length of movement traces (streaks) of drifting debris that appear in the generated SAR image. Hereinafter, this feature and other accompanying features will be described with reference to embodiments as examples.

[0026] In the following description, "azimuth" and "azimuth direction" mean the direction along the orbit (traveling direction) of the SAR satellite, which is also referred to as the along-track direction, orbit direction, or azimuth direction. "Range" and "range direction" mean the direction in which the SAR satellite irradiates microwaves obliquely downward toward the ground surface, which is usually a direction orthogonal to the azimuth direction. The range direction is also referred to as the distance direction, LOS (Line Of Sight) direction, or cross-track direction. In addition, "surveying" in the following description is not limited to the meaning defined in the Survey Act, and also includes measuring and observing the water surface state and shape (waterline), surface flow velocity, drifting debris and movement traces thereof of rivers and the like.

[0027] <OUTLINE OF SAR SYSTEM> FIG. 1 is a schematic diagram showing the basic functions of the SAR satellite 1 used in this example. FIG. 2 is a schematic diagram showing the types of imaging modes of the SAR satellite 1. Hereinafter, the mechanism of ground observation by the spotlight mode will be briefly described with reference to FIG. 1 and FIG. 2. It should be noted that detailed specifications not mentioned in this example may be considered to be identical or similar to those of known SAR systems.

[0028] As shown in Figure 1, the SAR satellite 1 in this example (hereinafter also simply referred to as "the satellite") is a system that uses the movement of the satellite itself to synthesize a virtual aperture (SA) that is wider than the actual aperture (RA) of the antenna equipped on the satellite, thereby obtaining high-resolution radar images. SAR satellite 1 irradiates microwave pulses in the range direction (R) perpendicular to its azimuth direction (AZ). SAR satellite 1 measures the distance from the satellite to the Earth's surface by measuring the time difference between when the pulse is emitted, when it is reflected (scattered) by the Earth's surface, and when the reflected wave returns. While moving at high speed in orbit, SAR satellite 1 repeatedly emits and receives microwaves continuously from different positions. By synthesizing the multiple data obtained in this way, it creates an effect as if the observation was made all at once by a giant antenna. This achieves a resolution (SA Res) higher than the azimuth resolution (RA Res) obtained by an actual antenna.

[0029] Furthermore, SAR satellite 1 in this example is a single-channel X-band SAR satellite. The reason for choosing a single-channel configuration is that most current SAR systems consist of a constellation of single-channel SAR satellites. The surveying method, which is possible with single-channel SAR satellites, makes it possible to grasp the condition of rivers more quickly when necessary. In addition, the X-band is the shortest wavelength frequency band (2.4-3.8 cm) in current SAR systems, and its microwaves have the characteristic of being easily scattered by the surface of objects. This surface scattering characteristic enables extremely high spatial resolution, making it possible to capture the movement traces of small floating objects such as vegetation debris and air bubbles drifting on the river.

[0030] As shown in Figure 2, SAR satellite 1 offers multiple imaging modes. These modes are classified according to how SAR satellite 1 controls the direction of its beam (microwave pulse). Each mode has different characteristics in terms of the range of observation and the resolution (fineness) of the image. In the "strip map mode" shown in Figure 2(a), SAR satellite 1 flies with the direction of its antenna fixed in the azimuth direction. As the satellite moves, it continuously observes the ground in a strip, allowing it to observe a wide area at once. However, its resolution is lower (standard) compared to the other two modes described later. The "steering spotlight mode" shown in Figure 2(b) is the mode that provides the highest resolution among the modes shown in the figure. In steering spotlight mode, while the satellite is moving, the direction of the antenna beam is rotated from the rear to the front in the azimuth direction, continuously irradiating only a specific narrow area (spot) on the ground with microwaves. By observing the same location from different angles for extended periods, the synthetic aperture can be lengthened, improving the azimuth resolution to sub-meter levels. However, the observable range becomes narrower. The "sliding spotlight mode" shown in Figure 2(c) is a mode with properties intermediate between the two modes described above. This mode also rotates the beam, similar to the steering spotlight mode, but instead of fixing the center of the beam to a specific point, it observes the ground by gradually sliding it in the direction of travel. This allows for observation of a wider area than the steering spotlight mode while maintaining a higher resolution than the strip map mode. In this example, "spotlight mode" includes both the steering spotlight mode and the sliding spotlight mode.

[0031] Figure 3 is a schematic diagram illustrating how point images appear in a SAR image (hereinafter simply referred to as "the image") of a moving object 2 moving in the azimuth direction. Figure 4 is a schematic diagram illustrating how point images appear in a SAR image of a moving object 2 moving in the range direction. Below, the blurring and positional shift of point images, which are characteristic of SAR images, will be explained with reference to Figures 3 and 4.

[0032] As described above, in SAR images, features are focused based on the change in Doppler frequency due to the movement of SAR satellite 1, and stationary feature points appear in the correct positions on the image. In other words, SAR images are generated under the assumption that all target objects are stationary. Objects moving during imaging (moving object 2) produce an additional frequency shift due to the velocity of moving object 2 itself, in addition to the Doppler effect due to the satellite's movement, resulting in a change in Doppler frequency that differs from that expected for stationary features. Typically, the direction of movement of moving object 2 includes both azimuth and range components.

[0033] When an object is moving during imaging, the echo diffuses in the image, preventing it from being properly focused. As a result, the point image 3 of the moving object 2 appears as a defocused (smeared) image in the SAR image. Specifically, as shown in Figures 3(a) and 3(b), the point image 3 of the moving object 2 moving in the azimuth direction appears as a long, elongated trajectory (streak 31) along its direction of movement. In particular, in imaging where high resolution is obtained with long illumination times, such as in spotlight mode, the streak 31 of the moving object 2 remains relatively clear in the image. The length of this streak 31 can be considered to be basically equal to the actual distance the moving object 2 moved during imaging. By dividing this distance by the imaging time (seconds), the speed of movement of the moving object 2 can be calculated.

[0034] Furthermore, as shown in Figures 4(a) and 4(b), if the movement of the mobile object 2 includes components of approaching or moving away from the SAR satellite 1 in the range direction, the Doppler frequency shifts, and the position of the point image 3 of the mobile object 2 on the image shifts from its original position in the azimuth direction (displacement, azimuth shift). More specifically, if the mobile object 2 is range-in (-R), i.e., approaching the SAR satellite 1, the position of the mobile object 2 on the image shifts toward the direction of movement of the SAR satellite 1 (towards the side with a higher Doppler frequency), and if it is range-out (+R), i.e., moving away, it shifts toward the opposite direction. The amount of azimuth shift of the point image 3 can be calculated using the following formula. For example, if the ground velocity of the SAR satellite 1 is 7 km / s and the slant range (straight-line distance between the SAR satellite 1 and the mobile object 2) is 650 km, then if the mobile object 2 is moving at 1 m / s in either the range direction, the point image 3 will appear shifted by about 90 m in the azimuth direction.

number

[0035] <Method for estimating surface flow velocity> Figure 5 is a flowchart showing a series of steps for estimating the surface velocity of a river using the surveying method of this example. "SAR image acquisition (S1)" in Figure 5 is an example of the acquisition process of the present invention. "Identification of movement traces (S2)" and "Restoration of streaks (S3)" are examples of the movement trace detection process of the present invention. "Calculation of movement speed (S4)" is an example of the flow velocity calculation process of the present invention. Figure 6 is a schematic diagram showing how streaks 31 appear on a SAR image when river debris 21 is moving in a straight line at a constant velocity.

[0036] As described above, the movement speed of the moving object 3 can be calculated from the relationship between the length of the streak 31 on the SAR image and the time it was captured. By applying this principle to floating objects drifting down a river and considering the movement speed of the floating objects as the surface velocity of the river, it becomes possible to estimate the surface velocity of the river from the SAR image. The procedure for estimating the surface velocity of a river using the surveying method in this example will be explained below with reference to Figures 5 and 6. Note that the example in Figure 6 is an extremely simple example, so some of the steps in Figure 5 are omitted. Also, "floating objects" here refer to natural objects such as driftwood or artificial objects such as household items that do not have a self-position detection function by GNSS, etc., in other words, "garbage". In some cases, it may be possible to intentionally release (let drift) objects suitable for the surveying method in this example.

[0037] In this surveying method, first, the river to be surveyed is photographed in the X-band using the spotlight mode of SAR satellite 1 to generate a SAR image (S1). As shown in Figure 6(a), if the floating debris 21 in the river was flowing in the azimuth direction during the shooting, that is, if the floating debris 21 was moving parallel to SAR satellite 1, the movement trace of the floating debris 21 will appear in the SAR image as a streak 31 simply stretched in the azimuth direction. Once the SAR image is generated, the streaks 31 of the floating debris 21 are identified visually or by image analysis processing (S2). The brightness of each point image in the SAR image is determined by its intensity (square of the amplitude). In addition, information indicating the backscatter characteristics of the target object is recorded in each pixel of the SAR image. This is the property of the target object, which is determined by its electrical properties (dielectric constant, conductivity), surface roughness, structural shape, water content, etc. For example, it is conceivable to extract the floating debris 21 from areas where this intensity and backscatter characteristics differ from those of the water surface. In the example shown in Figure 6(a), the floating object 21 is moving in a straight line at a constant velocity, and its streak 31 appears as an almost continuous line segment. Therefore, its total length can be measured without going through the subsequent restoration process (S3). Once the total length of the streak 31 is obtained, the movement speed of the floating object 21 can be calculated by dividing the length of the line segment by the shooting time in spotlight mode (S4). The movement speed obtained here can be considered as the surface current velocity of the river being surveyed. In the example shown in Figure 6(a), the surface current velocity of the river was calculated using one streak 31 of one floating object 21, but the movement speed can be calculated for multiple floating objects 21 using the same procedure, and the average of these can be used as the surface current velocity of the river. In this invention, "average" includes weighted averages and harmonic averages that are weighted by the brightness (intensity) of the streak 31, etc.

[0038] As shown in Figure 6(b), if the direction of movement of the drifting object 21 includes a component in the range direction, the position of its streak 31 will be shifted in the azimuth direction, as explained in Figure 4. In the example of Figure 6(b), since the drifting object 21 is moving toward the SAR satellite 1, the streak 31 formed by its point image 3 appears shifted toward the direction of movement of the SAR satellite 1. However, even with this shift, the length of the streak 31 still represents the distance the drifting object 21 has moved. Therefore, by dividing the total length of the streak 31 by the imaging time in spotlight mode, the velocity of the drifting object 21 can be calculated, similar to the example in Figure 6(a) (S4).

[0039] Next, Figure 7 is a schematic diagram showing how streaks 31 appear on SAR images in a meandering river. The exposure time for the same area using spotlight mode is typically around 10 to 30 seconds. When a river meanders, that is, when the flow direction differs in different parts of the river, floating debris moving downstream changes its direction of movement according to the flow direction in each part. In particular, when a river meanders with a large curvature, the direction of movement of the floating debris changes abruptly, which can cause the streak of the debris to be divided into multiple segments on the SAR image. In order to calculate the movement speed of the debris from the length of the streak, it is necessary to combine these divided segments to reconstruct a series of streaks.

[0040] The streak 31 of the floating object 21 shown in Figure 7 is divided into a total of five segments 31a to e. Here, in addition to the floating object 21 shown, other floating objects may also be present in the river. In particular, a wide variety of objects are expected to be flowing in a river during a flood. Identifying the segments formed by the floating object 21 from among these and reconstructing them into a series of streaks 31 is not easy. Therefore, in this example, the accuracy of reconstructing the streak 31 is improved based on various clues, as explained below.

[0041] Referring again to Figure 5, the procedure for reconstructing the streak 31 in the example in Figure 7 will be explained. First, by obtaining the backscattering characteristics from the point images of segments 31a to e (S31), the drifting objects 21 that formed each segment 31a to e can be identified to some extent. In other words, segments that are highly likely to have been formed by the same drifting object can be grouped together. However, drifting objects are expected to rotate and tumble in the flow. Backscattering characteristics are sensitive to the orientation (aspect angle) of the target object. For example, even the same piece of driftwood can have a large change in apparent brightness if it rotates during observation. In other words, even a point image generated from a single piece of drifting object will not have a constant backscattering characteristic. Therefore, the characteristics of the change in backscattering characteristics should be picked up from at least the multiple point images 3 that make up the segment.

[0042] Furthermore, the flow direction in each part of the river being observed can usually be easily determined from the river's location and shape, or is known in advance. In other words, the direction to which segments 31a to 31e of the floating debris 21 flowing down the river are shifted in the azimuth direction can be determined to some extent from their flow direction. For example, in the case of Figure 7(a), segment 31b, which is moving away from SAR satellite 1, is shifted to the upper side of the figure, and segment 31d, which is approaching SAR satellite 1, is shifted to the lower side of the figure. By taking this shift direction into account (S32), the accuracy of segment grouping can be improved. Using these backscattering characteristics and shift directions as clues, it is possible to narrow down the candidates for the original segment from which each segment was separated.

[0043] Subsequently, by joining the endpoints of segments 31a to e, which are highly likely to have been formed by the same drifting object 21 (S35), a series of streaks 31 as shown in Figure 7(b) is reconstructed (S3).

[0044] After the streak 31 is restored, the speed of the drifting object 21 can be calculated by dividing the total length of the streak 31 by the shooting time in spotlight mode, similar to the example in Figure 6 (S4).

[0045] Ideally, this series of processes should be automated through image analysis and data processing, but for the time being, it may be carried out semi-automatically with visual inspection and human judgment interspersed. Once a sufficient amount of work experience (training dataset) has been accumulated, full automation will naturally be possible. Also, the longer the shooting time in spotlight mode, the higher the possibility that the direction of movement of the drifting object 21 will change midway (the streak 31 will be broken up). Therefore, it is desirable to set the shooting time in spotlight mode to the shortest time at which the streak 31 can be detected. For this reason, the shooting time at the same location in spotlight mode should be set to 10 seconds or less, or even less than 10 seconds if possible (for example, 6 to 8 seconds). This increases the appearance rate of streaks 31 moving in a straight line at a constant velocity, making it easier to estimate the surface flow velocity.

[0046] <Variation> Modifications of the above embodiment will be described below with reference to Figure 5. In the above embodiment, the surface flow velocity of the river was estimated using a geometric approach from the length of the streak 31 formed by shooting in spotlight mode, but it is also possible to obtain the direction of movement and velocity of each point image on the SAR image using other physical methods.

[0047] For example, if the SAR image is a Single Look Complex (SLC) or raw data, that is, if it contains phase information, the azimuth velocity of a blurred point image can be determined using the MTR method. More specifically, by extracting movement traces from the SAR image (removing clutter) and searching for the azimuth velocity that maximizes the clarity of these traces, the azimuth velocity of that point image can be determined (S33). For example, by calculating the azimuth velocity of point images that should be moving in parallel with SAR satellite 1 in each part of a river using this method, the approximate surface flow velocity of that river can be estimated. Using this information as a clue, it becomes possible to narrow down the candidates for the original segment from which each segment was divided with higher accuracy. Note that "MTR (Motion Target Refocusing)" here is a general term for techniques that search for the azimuth velocity that maximizes the clarity of movement traces. Specific algorithms include, for example, FrFT (Fractional Fourier Transform), PSR (Parametric Sparse Representation), ROPE (Range and azimuth Optimal Parameter Estimation), and ISAR (Inverse SAR). Other possible approaches include Phase Gradient Autofocus (PGA), back projection, and Map-Drift Autofocus (MDA).

[0048] Furthermore, in the above embodiment, a single-channel SAR satellite is used to enable quicker assessment of river conditions when necessary. However, by using an along-track multi-channel SAR satellite, the direction and velocity of the range movement of the point image can be calculated from the interference phase of the point image, which is the trace of movement, for example, using along-track interferometry (ATI) (S34). An along-track multi-channel synthetic aperture radar satellite is a SAR satellite with multiple antennas arranged along the azimuth direction of the satellite. These antennas observe the same point with a very small time difference. When the target object is stationary, these return signals are in almost the same phase, but when the target object is moving, a phase shift (interference phase) occurs. By measuring this phase shift, the direction and velocity of the range movement of the moving object can be calculated with high precision. This makes it possible to identify which side and by how much the point image has shifted from its original position in the azimuth direction. Using this information as a clue, it becomes possible to narrow down the candidates for the original segment of each segment with higher precision.

[0049] <Other Embodiments> Figure 8 is a flowchart showing a series of steps for estimating the surface velocity of a river according to another embodiment of the present invention. "SAR image acquisition (S1)" in Figure 8 is an example of the acquisition process of the present invention. "Identification of movement traces (S2)" is an example of the movement trace detection process of the present invention. "Calculation of movement velocity (S4)" is an example of the flow velocity calculation process of the present invention. The main feature of this example is that it calculates the movement velocity of point images on the SAR image by using the MTR and / or ATI of the above modified example, and considers that movement velocity to be the surface velocity of the river. Points not mentioned in this example may be considered to be the same as or similar to those of the above embodiment.

[0050] The procedures for "capturing SAR images (S1)" and "identifying movement traces (S2)" in this example are the same as in the above embodiment, so their explanation will be omitted.

[0051] In this example's surveying method, after identifying the movement trace, the azimuth direction movement velocity of the point image is calculated using MTR (S41), and the range direction movement velocity of the point image is calculated using ATI (S42). From these azimuth and range direction movement components, the final movement direction and movement velocity of the point image are calculated, and the movement velocity obtained here is considered to be the surface flow velocity of the river being surveyed. In this example, the movement velocity may also be calculated for multiple movement traces and point images using the same procedure, and the average of these may be used as the surface flow velocity of the river. Thus, in this example, the movement direction of the point image is determined based on the information contained in each point image, rather than the length of the streak. Therefore, in this example, shooting in spotlight mode is not essential, and it can also be applied to SAR images taken in strip map mode.

[0052] Furthermore, if a multi-channel SAR satellite is used, the azimuth velocity of the point image can be calculated using existing technologies such as a Displaced Phase Center Antenna (DCPA) or Space-Time Adaptive Processing (STAP), instead of using an MTR.

[0053] Although embodiments of the present invention have been described above, the scope of the present invention is not limited thereto, and various modifications can be made without departing from the spirit of the invention. For example, although the above embodiments were intended for the estimation of surface flow velocity of rivers, the surveying method of the present invention can also be applied to other flowing water areas such as mountain streams, waterways, ravines, and floodwaters. [Explanation of Symbols]

[0054] 1:SAR satellite 2: Mobile 21: Flotsam 3: Point image (movement trace) 31: Streak (movement track) 31a~e: Segments (movement traces)

Claims

[Claim 1] The imaging process involves using a synthetic aperture radar satellite in spotlight mode, steering spotlight mode, or sliding spotlight mode to image a body of water. A movement trace detection step is performed to identify the movement traces of floating objects that flowed down the water area during the shooting process, based on the image of the water area captured in the shooting step. The process includes a flow velocity calculation step of calculating the movement speed of the drifting object from the length of the aforementioned movement trace, The movement trace detection step includes the step of joining the endpoints of the movement trace formed by the same drifting material, which has been divided into segments. The surface flow velocity of the water area during the filming process is estimated from the movement speed of the floating object, or the average of the movement speeds of multiple floating objects. Surveying method.

Images