Efficient statistical classification of satellite measurements

TL;DR

This paper introduces an efficient kernel-based classification algorithm called Adaptive Gaussian Filtering for satellite image analysis, offering high accuracy and real-time processing capability for various remote sensing applications.

Contribution

The paper presents a novel adaptive Gaussian filtering algorithm that improves classification efficiency without sacrificing accuracy, outperforming existing methods like KNN, LVQ, and SVM.

Findings

Superior efficiency over traditional methods

Maintains high classification accuracy

Provides conditional probability estimates

Abstract

Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of modern satellite instruments and because of the requirement for real-time processing, any algorithm has to be fast to be useful. Here we describe an algorithm based on kernel estimation called Adaptive Gaussian Filtering that incorporates several innovations to produce superior efficiency as compared to three other popular methods: k-nearest-neighbour (KNN), Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). This efficiency is gained with no compromises: accuracy is maintained, while estimates of the conditional probabilities are returned. These are useful not only to gauge the accuracy of an estimate in the absence of its true value, but also to re-calibrate a retrieved image and as a proxy for a discretized continuum variable. The algorithm is demonstrated and compared with the other three on a pair of synthetic test classes and to map the waterways of the Netherlands. Software may be found at: http://libagf.sourceforge.net.