EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
This provides an accessible tool for remote sensing researchers to perform semantic segmentation on hyperspectral imagery, addressing a data annotation bottleneck in the field.
The paper tackles the lack of annotated data for semantic segmentation of non-RGB remote sensing imagery by introducing EarthMapper, a software pipeline that includes self-taught feature extraction and various classifiers, achieving state-of-the-art performance on the Indian Pines and Pavia University datasets.
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor specific datasets is time intensive and cost prohibitive, remote sensing researchers have embraced deep unsupervised feature extraction. Although these methods have pushed state-of-the-art performance on current HSI benchmarks, many of these tools are not readily accessible to many researchers. In this letter, we introduce a software pipeline, which we call EarthMapper, for the semantic segmentation of non-RGB remote sensing imagery. It includes self-taught spatial-spectral feature extraction, various standard and deep learning classifiers, and undirected graphical models for post-processing. We evaluated EarthMapper on the Indian Pines and Pavia University datasets and have released this code for public use.