Land Use and Land Cover Classification with SPOT-5 Images and Partial Lanczos Extreme Learning Machine (PL-ELM)

摘要

Satellite remote sensing technology and the science associated with evaluation of land use and land cover (LULC) in urban region makes use of the wide range images and algorithms. Yet previous processing with LULC methods is often time-consuming, laborious, and tedious making the outputs unavailable within the required time window. This paper presents a new image classification approach based on a novel neural computing technique that is applied to identify the LULC patterns in a fast growing urban region with the aid of 2.5-meter resolution SPOT-5 image products. Since some different classes of LULC may be linked with similar spectral characteristics, texture features and vegetation indexes are extracted and included during the classification process to enhance the discernability. The classifier is constructed based on the partial lanczos extreme learning machine (PL-ELM), which is a novel machine learning algorithm with fast learning speed and outstanding generalization performance. A validation procedure based on ground truth data and comparisons with some classic classifiers prove the credibility of the proposed PL-ELM classification approach in terms of the classification accuracy as well as the processing speed. It may be applied for 'rapid change detection' in urban region for regular emergency response, regular planning, and land management in the future.