Multi-Target Multiple Instance Learning for Hyperspectral Target Detection

TL;DR

This paper introduces two novel multiple-instance learning methods for hyperspectral target detection that effectively learn target signatures from imprecise labels, improving detection accuracy in challenging remote sensing scenarios.

Contribution

The paper proposes Multi-Target MI-ACE and MI-SMF, innovative algorithms that handle imprecise labels and learn target signatures directly from hyperspectral data.

Findings

Effective target signature learning demonstrated on simulated data

Successful detection performance on real hyperspectral datasets

Algorithms outperform traditional methods in imprecise labeling conditions

Abstract

In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site's spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an image's spatial resolution. We propose an approach, with two variations, that estimates multiple target signatures from training samples with imprecise labels: Multi-Target Multiple Instance Adaptive Cosine Estimator (Multi-Target MI-ACE) and Multi-Target Multiple Instance Spectral Match Filter (Multi-Target MI-SMF). The proposed methods address the problems above by directly considering the multiple-instance, imprecisely labeled dataset. They learn a dictionary of target signatures that optimizes detection against a background using the Adaptive Cosine Estimator (ACE) and Spectral Match Filter (SMF). Experiments were conducted to test the proposed algorithms using a simulated hyperspectral dataset, the MUUFL Gulfport hyperspectral dataset collected over the University of Southern Mississippi-Gulfpark Campus, and the AVIRIS hyperspectral dataset collected over Santa Barbara County, California. Both simulated and real hyperspectral target detection experiments show the proposed algorithms are effective at learning target signatures and performing target detection.