The processing of satellite imagery is dependent upon the quality of imagery. Due to low resolution, it is difficult to extract accurate information according to the requirements of applications. For the purpose of vehicle detection under shadow regions, we have used HOG for feature extraction, SVM is used for classification and HOG is discerned worthwhile tool for complex environments. Shadow images have been scrutinized and found very complex for detection as observed very low detection rates therefore our dedication is towards enhancement of detection rate under shadow regions by implementing appropriate preprocessing. Vehicles are precisely detected under non-shadow regions with high detection rate than shadow regions.
Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.