Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Abstract

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

“…To this end, a wide variety of features, such as spectral features (spectral reflectance and spectral indices), textural features (calculated by the gray level co-occurrence matrix), and vegetation abundances (the abundances of coniferous forest, broad-leaved forest, and low vegetation, obtained by LSMA) were derived from the Sentinel-2A image data and combined with topographical features (DEM-digital elevation model, and slope and aspect derived from DEM) to classify urban vegetation classification using the support vector machine (SVM) method. SVM is a machine learning algorithm used for image classification [44,45] and can achieve high accuracy. We compared SVM with other classifiers, namely random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST), and found that the SVM produced the best result when vegetation abundances were added for classification.…”

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

“…The results reveal similar conclusions to the first experiment presented above. First, although the classification accuracy of SVM can be improved by selecting optimal training samples to enhance the generalization performance of classifier [61][62][63]. In our work, SVM was observed to have the worst classification accuracy with the random non-optimized training samples.…”

Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution

“…We evaluated the proposed approach using two publicly available HS imaging database: the CAVEdataset [37] and the Harvard dataset [38], and three real satellite images including the Hyperspec-VNIR Chikusei image [39], Salinas, and University of Pavia scenes [40][41][42]. The CAVE dataset consists of 32 indoor images including paintings, toys, food, and so on, captured under controlled illumination, and the Harvard dataset has 50 indoor and outdoor images recorded under daylight illumination.…”