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