Evaluation of essential design criteria for IRST systems

Kohnle, Anton; Neuwirth, Ruland; Schuberth, W.; Stein, Karin; Hoehn, D. H.; Gabler, Richard; Hofmann, Lothar; Euing, Wilfried

doi:10.1117/12.160530

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filter is especially suited for the detection of thermal sources (hot spots), e.g. from in IR images [4]. In the left intensity image the left object seems to be warmer as the right vehicle, but e.g.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Feature evaluation for target/background discrimination in image sequences taken by approaching sensors

2010

View full text Add to dashboard Cite

The conspicuity of different targets in image sequences taken by approaching sensors is addressed in applications such as the assessment of camouflage effectiveness or the performance evaluation of autonomous systems. In such evaluation processes the consideration of background characteristics is essential due to the propensity to confuse target and background signatures. Several discriminating features of target and background signature can be derived. Furthermore, the changing aspect and spatial resolution during an approach on a target have to be taken into account. Considering salient points in image sequences, we perform a nominal/actual value comparison by evaluating the receiver operating characteristic (ROC) curve for the detections in each image. Hence, reference regions for targets and backgrounds are provided for the entire image sequence by means of robust image registration. The consideration of the uncertainty for the temporal progression of the ROC curve enables hypothesis testing for well-founded statements about the significance of the determined distinctiveness of targets with respect to their background. The approach is neither restricted to images taken by IR sensors nor applicable to low level image analysis steps only, but can be considered as a general method for the assessment of feature evaluation and target distinctiveness. The analysis method proposed facilitates an objective comparison of object appearance with both, its relevant background and other targets, using different image analysis features. The feasibility and the usefulness of the approach are demonstrated with real data recorded with a FLIR sensor during a field trial on a bare and mock-up target

show abstract

Section: Feature Extraction Methodsmentioning

confidence: 99%

Feature evaluation for target/background discrimination in image sequences taken by approaching sensors

2010

View full text Add to dashboard Cite

show abstract

“…For detection of cues for cars a spot detector [8] is used. For each pixel p=(x,y) it looks for the maximum gray value m d along the margin of a square of radius d (Pixels (x+j,y+i), where Max (|i|, |j|)=d).…”

Section: A Iconic Spot Detectionmentioning

confidence: 99%

Estimating urban activity on high-resolution thermal image sequences aided by large scale vector maps

Michaelsen

Stilla²

IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas (Cat. No.01EX482)

View full text Add to dashboard Cite

In this contribution we describe a method to assess the activity of vehicles based on airborne image sequences from an infrared camera. At the resolution of approximately one meter vehicles appear as elongated spots. In urban areas many additional other objects have the same property. To discriminate vehicles from these objects we fuse information from IR-images and vector-maps. Besides evidence from overlapping areas of frames of an IR-sequence is accumulated in the scene. We also perform grouping of vehicles into rows along the margins of the roads. These tasks can be automatically performed by a production net. The generic productions for grouping become feasible through the use of map knowledge as context.

show abstract

“…We extract these features by modified spot filter in this step. The spot filter [48,49] is a robust point feature detector that can be used in SAR images. Figure 5a shows the mask of this filter in the case of three shells.…”

Section: Extraction Of Salient Featuresmentioning

confidence: 99%

Individual High-Rise Building Extraction from Single High-Resolution SAR Image Based on Part Model

Liu,

Li,

Hong

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Building extraction plays an important role in urban information analysis, which is helpful for urban planning and disaster monitoring. With the improvement of SAR resolution, rich detailed information in urban areas is revealed, but the discretized features also pose challenges for object detection. This paper addresses the problem of individual high-rise building extraction based on single high-resolution SAR image. Different from previous methods that require building facades to be presented in specific appearances, the proposed method is suitable for extraction of various types of high-rise buildings. After analyzing the SAR images of many types of high-rise buildings, we establish a unified high-rise building part model, on the basis of a scattering mechanism of building structures, to describe the facade characteristics of high-rise buildings, including a facade regularity part, facade bright line part, double bounce part, and their spatial topological relationships. A three-level high-rise building extraction framework, named HRBE-PM, is proposed based on the high-rise building part model. At the pixel level, a modified spot filter is used to extract bright spots and bright lines of different scales simultaneously to obtain salient features. At the structure level, building parts are generated based on the salient features according to semantic information. At the object level, spatial topological information between parts is introduced to generate building hypotheses. We define two indicators, completeness and compactness, to comprehensively evaluate each building hypothesis and select the optimal ones. After postprocessing, the final high-rise building extraction results are obtained. Experiments on two TerraSAR-X images show that the high-rise building extraction precision rate of the HRBE-PM method is above 85.29%, the recall rate is above 82.95%, and the F1-score is above 0.87. The results indicate that the HRBE-PM method can accurately extract individual high-rise buildings higher than 24 m in most dense scenes, regardless of building types.

show abstract

Evaluation of essential design criteria for IRST systems

Cited by 7 publications

References 0 publications

Feature evaluation for target/background discrimination in image sequences taken by approaching sensors

Feature evaluation for target/background discrimination in image sequences taken by approaching sensors

Estimating urban activity on high-resolution thermal image sequences aided by large scale vector maps

Individual High-Rise Building Extraction from Single High-Resolution SAR Image Based on Part Model

Contact Info

Product

Resources

About