A shadow detection and extraction algorithm using digital elevation models and x‐band weather radar measurements

Young, Steve; Kakarlapudi, Swarna; Haag, Maarten Uijt de

doi:10.1080/01431160512331337772

Cited by 15 publications

(11 citation statements)

References 3 publications

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“…The FSM for this particular thread consists of 3 possible states, and is driven by the condition of a range bin from the weather radar sensor reflectivity results. The condition stipulates that the reflectivity results are either zero for no reflectivity or some positive number for some reflectivity and this is represented by the state transition condition G. Therefore, the three possible states a range bin can assume in this thread adapted from [7] are the A, B, and C states which correspond to a range bin being in the initial state, greater than zero, and equal to zero respectively. As a result, the FSM result of A, B, and C is classified as -3, 1, and 0 respectively.…”

Section: Weather Radar Threadmentioning

confidence: 99%

“…The DEM thread employs a model of the WxR measurement scheme in its extraction and use of the digital terrain elevation stored data [7]. The whole process of the DEM thread can be broken down into 4 steps as opposed to the 2 steps in WxR thread.…”

Section: Digital Elevation Model Threadmentioning

confidence: 99%

“…In this case, the longitudes and latitudes coordinate pairs are used to retrieve the corresponding terrain elevation from the stored DEM. Once the elevations are known, a two dimensional model of the WxR scheme mentioned earlier is used to compute the depression angles [7] 26U20L2 0b2 MXE…”

Section: Digital Elevation Model Threadmentioning

confidence: 99%

“…The basis of the integrity monitoring approach is adapted from the algorithm proposed by Young et al [7][8] [9]. This approach works as a quality metric in synthetic vision systems by evaluating the integrity of stored Digital Elevation Models (DEM) data against real-time sensed X-band Weather Radar measurements, Global Positioning System (GPS) information, and the Inertial Reference Unit (IRU) data.…”

Section: Integrity Monitoring Approachmentioning

confidence: 99%

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Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

et al. 2006

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The integrity monitor for synthetic vision systems provides pilots with a consistency check between stored Digital Elevation Models (DEM) and real-time sensor data. This paper discusses the implementation of the Shadow Detection and Extraction (SHADE) algorithm in reconfigurable hardware to increase the efficiency of the design. The SHADE algorithm correlates data from a weather radar and DEM to determine occluded regions of the flight path terrain. This process of correlating the weather radar and DEM data occurs in two parallel threads which are then fed into a disparity checker. The DEM thread is broken up into four main sub-functions: 1) synchronization and translation of GPS coordinates of aircraft to the weather radar, 2) mapping range bins to coordinates and computing depression angles, 3) mapping state assignments to range bins, and 4) shadow region edge detection. This correlation must be done in realtime; therefore, a hardware implementation is ideal due to the amount of data that is to be processed. The hardware of choice is the field programmable gate array because of programmability, reusability, and computational ability. Assigning states to each range bin is the most computationally intensive process and it is implemented as a finite state machine (FSM). Results of this work are focused on the implementation of the FSM.

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Section: Weather Radar Threadmentioning

confidence: 99%

Section: Digital Elevation Model Threadmentioning

confidence: 99%

Section: Digital Elevation Model Threadmentioning

confidence: 99%

Section: Integrity Monitoring Approachmentioning

confidence: 99%

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Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

et al. 2006

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“…[19][20][21] The DEM shadow simulation could also help radar measurements for aviation safety. 22 However, because of heavy computational requirements and the lack of fine resolution of DEMs in mountainous regions, the DEM approach may cause difficulties for topographic correction.…”

Section: Introductionmentioning

confidence: 99%

Calibrated ratio approach for vegetation detection in shaded areas

Kao

Ren

Lee

2014

J. Appl. Remote Sens

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Abstract. Removing shadow effects remains a challenge in processing optical remote sensing data. Shadows occur because of obstructions from the terrain topography or cloud cover, which can cause errors for image classification. Shadow effects can be removed using a band-ratio approach because the shaded areas in optical images have a nearly proportional variation in the bands. We developed a calibrated band-ratio approach for shadow reduction. Before the ratio approach was applied, a regression technique was used to obtain information for calibration from the relative sensor gain and offset. After calibration, the ratio vegetation index (RVI) band ratios were calculated to process the image data, which can simultaneously remove the shadow effects and assist the search for vegetation. Real and synthesized images show that the calibratedratio approach can improve vegetation detection compared with standard RVI and dark pixel subtraction approaches.

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Mapping shadows in very high-resolution satellite data using HSV and edge detection techniques

et al. 2013

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A shadow detection and extraction algorithm using digital elevation models and x‐band weather radar measurements

Cited by 15 publications

References 3 publications

Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

Calibrated ratio approach for vegetation detection in shaded areas

Mapping shadows in very high-resolution satellite data using HSV and edge detection techniques

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