High-resolution satellite images contain a huge amount of information. Shadows in such images generate real problems in classifying and extracting the required information. Although signals recorded in the shadow areas are weak, it is still possible to recover them. Significant work is already done in the shadow detection direction but, classifying shadow pixels from vegetation pixels correctly is still an issue as dark vegetation areas are still misclassified as shadow in some cases. Background subtraction is a commonly used method to detect moving objects from videos captured by static cameras. However, shadows and reflections significantly affect the output of background subtraction algorithms and distort the shape of the objects obtained as a result. Thus, shadow detection and removal is a crucial post-processing step to perform accurate object tracking required by different applications. We present a lightweight method to detect and remove shadows as well as reflection effects in indoor and outdoor environments by using spatial and spectral features. This method incorporates an adaptive way to set thresholds to avoid preset numbers. We present a comparison of the outputs we obtained with those of several other methods. The experimental results demonstrate the success of the proposed algorithm.
- Principal component
- Shadow threshold.
- In appearance-based methods, less accurate features description because of whole
- In geometric based methods, the geometric features like the distance between eyes, face
length and width, etc., are considered which does not provide optimal results
- Adaptive Local Filter
- Contrast Limited Adaptive Histogram Equalization
- Queue forming
- Identification of shadow
- Mat lab 7.5 and above versions
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