15 October 1993 Neural network filter for target detection in high-clutter imagery
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The detection of objects in high-resolution aerial imagery has proven to be a difficult task. In our application, the amount of image clutter is extremely high. Under these conditions, detection based on low-level image cues tends to perform poorly. Neural network techniques have been proposed in object detection applications due to proven robust performance characteristics. A neural network filter was designed and trained to detect targets in thermal infrared images. The feature extraction stage was eliminated and raw gray-levels were utilized as input to the network. Two fundamentally different approaches were used to design the training sets. In the first approach, actual image data was utilized for training. In the second case, a model-based approach was adopted to design the training set vectors. The training set consisted of object and background data. The backpropagation training algorithm was modified to improve network convergence and speed and used to train the network. The neural network filter was tested extensively on real image data. Detection rates were determined for varying false alarm rates in each case. The detection and false alarm rates were excellent for the neural network filters. Their overall performance was much superior to that of the size- matched contrast-box filter, especially in the images with higher amounts of visual clutter.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mukul V. Shirvaikar, Mukul V. Shirvaikar, Mohan M. Trivedi, Mohan M. Trivedi, } "Neural network filter for target detection in high-clutter imagery", Proc. SPIE 1960, Automatic Object Recognition III, (15 October 1993); doi: 10.1117/12.160591; https://doi.org/10.1117/12.160591


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