{'fraction_non_alphanumeric': 0.02280130293159609, 'fraction_numerical': 0.0016286644951140066, 'mean_word_length': 5.367875647668394, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model

Traditional prior-knowledge-based region of interest (ROI) detection methods for processing high-resolution remote sensing images generally use global searching, which largely leads to prohibitive computational complexity. As an attempt to solve this problem, in the present study, a faster, more efficient ROI detection algorithm based on an adaptive spatial subsampling visual attention model (ASS-VA) is proposed. In the ASS-VA model, a visual attention mechanism is used to avoid applying image segmentation and feature detection to the entire image. The adaptive spatial subsampling strategy is formulated to decrease the computational complexity of ROI detection. A discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. In addition, a region growing strategy is employed to obtain more accurate shape information of ROIs. Experimental results show that the time spent on detection using the new algorithm is only 2-4% of that expended in the traditional visual attention model and the detection results are visually more accurate.