An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification
Xiaodi Shang, Meiping Song, Chein‐I Chang
Abstract
Iterative constrained energy minimization (ICEM) has shown success in classification. However, a drawback suffered from ICEM is its requirement of complete ground truth to calculate class means. This letter develops an iterative selection of training samples to extend ICEM with two versions: iterative fixed training sampling constrained energy minimization (CEM) (IFTS-CEM) which uses a fixed training sample set throughout the entire iterative process and iterative random training sampling CEM (IRTS-CEM) which uses a random training sampling (RTS) at each iteration. The experimental results demonstrate that IRTS-CEM performs better than IFTS-CEM and also comparable to ICEM.
Topics & Concepts
MinificationIterative methodIterative and incremental developmentSampling (signal processing)Hyperspectral imagingComputer scienceSample (material)Energy minimizationSelection (genetic algorithm)Energy (signal processing)Training setArtificial intelligenceSet (abstract data type)AlgorithmMathematical optimizationMathematicsStatisticsComputer visionComputational chemistrySoftware engineeringFilter (signal processing)ChemistryChromatographyProgramming languageRemote-Sensing Image ClassificationRemote Sensing and Land UseImage Retrieval and Classification Techniques