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Small satellite constellations provide daily global coverage of the earth'slandmass, but image enrichment relies on automating key tasks like changedetection or feature searches. For example, to extract text annotations fromraw pixels requires two dependent machine learning models, one to analyze theoverhead image and the other to generate a descriptive caption. We evaluateseven models on the previously largest benchmark for satellite image captions.We extend the labeled image samples five-fold, then augment, correct and prunethe vocabulary to approach a rough min-max (minimum word, maximum description).This outcome compares favorably to previous work with large pre-trained imagemodels but offers a hundred-fold reduction in model size without sacrificingoverall accuracy (when measured with log entropy loss). These smaller modelsprovide new deployment opportunities, particularly when pushed to edgeprocessors, on-board satellites, or distributed ground stations. To quantify acaption's descriptiveness, we introduce a novel multi-class confusion or errormatrix to score both human-labeled test data and never-labeled images thatinclude bounding box detection but lack full sentence captions. This worksuggests future captioning strategies, particularly ones that can enrich theclass coverage beyond land use applications and that lessen color-centered andadjacency adjectives ("green", "near", "between", etc.). Many modern languagetransformers present novel and exploitable models with world knowledge gleanedfrom training from their vast online corpus. One interesting, but easy examplemight learn the word association between wind and waves, thus enriching a beachscene with more than just color descriptions that otherwise might be accessedfrom raw pixels without text annotation.

Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Pictures