{'fraction_non_alphanumeric': 0.022598870056497175, 'fraction_numerical': 0.005649717514124294, 'mean_word_length': 5.560185185185185, '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}}
Curvelet image processing of Landsat images

The remote sensing community using geo-synchronous satellites (called Landsat and its successors) faces twin challenges of forecasting earth's hazards (biomass changes and hydrolic changes like flood and drought) and processing the massive glut of information being collected. On the one hand, there are challenges with respect to sensors, multiple instruments, data fusion and data transport. On the other hand, there are challenges of predefining content: define content, pattern recognition and preprocessing. Wavelets have emerged as the single most versatile means for automatic georegistration. The author has used wavelet discontinuity detection to define boundaries in crops in Landsat images. Crops are planted as a square grid but are superimposed on the curved boundaries dictated by meandering rivers2'3'4'5. The digital curvelet transform9 has recently been introduced. It will be applied in this investigation to better define curved boundaries in Landsat images. Candes and Donoho9 developed a new multiscale transform which they called the curvelet transform designed to represent edges along curves. Wavelets, while working successfully with dimension 1, are less successful in dealing with 'edge' discontinuities in dimension 2. It is expected that this new transform will be valuable in areas like image processing, medical imaging, and remote sensing.