Abstract
Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.
Highlights
Urban land-use/land-cover (LULC) classification plays an important role in various applications, including urban change studies and urban planning [1]
This paper proposes optimal decision fusion for urban LULC classification based on adaptive differential evolution (ODF-ADE) to optimize the weights of the different classifiers for hyperspectral remote sensing imagery and light detection and ranging (LiDAR) data
Based on DE theory, this paper has proposed a new optimal decision fusion strategy for the fusion of hyperspectral images and LiDAR data, namely ODF-ADE
Summary
Urban land-use/land-cover (LULC) classification plays an important role in various applications, including urban change studies and urban planning [1]. This paper proposes optimal decision fusion for urban LULC classification based on adaptive differential evolution (ODF-ADE) to optimize the weights of the different classifiers for hyperspectral remote sensing imagery and LiDAR data. The classification maps are generated by the support vector machine (SVM) [37], the maximum likelihood classifier (MLC) [38] and multinomial logistic regression (MLR) [39], which have different advantages in dealing with samples of different distributions In line with this strategy, the weight optimization problem is transformed into an optimization problem in the feature space by maximizing the objective function, which is constructed using the minimum Euclidean distance between each pixel and the corresponding predicted class in the training samples.
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