The need for information of the Earth’s surface is growing as it is the base for applications such as monitoring the land uses or performing environmental studies, for example. In this context the effective change detection (CD) among multitemporal datasets is a key process that must produce accurate results obtained by computationally efficient algorithms. Most of the CD methods are focused on binary detection (presence or absence of changes) or in the clustering of the different detected types of changes. In this paper, a CUDA scheme to perform pixel-based multiclass CD for hyperspectral datasets is introduced. The scheme combines multiclass CD with binary CD to obtain an accurate multiclass change map. The combination with the binary map contributes to reducing the execution time of the CUDA code. The binary CD is based on performing the difference among images based on Euclidean and Spectral Angle Mapper (SAM) distances and a later thresholding by Otsu’s algorithm to detect the changed pixels. The multiclass CD begins with the fusion of the multitemporal data following with feature extraction by Principal Component Analysis (PCA) and incorporating spatial features by means of an Extended Morphological Profile (EMP). The resulting dataset is filtered using the binary CD map and classified pixel by pixel by the supervised algorithms Extreme Learning Machine (ELM) and Support Vector Machine (SVM). The scheme was validated in a non-synthetic multitemporal hyperspectral dataset.
Keywords: Hyperspectral Change Detection, Direct Multidate Classification, Extended Morphological Profiles, Segmentation, Spectral Angle Mapper, GPU, CUDA