High spatial resolution (HSR) image scene classification is aimed at bridging the semantic gap between low-level features and high-level semantic concepts, which is a challenging task due to the complex distribution of ground objects in HSR images. Scene classification based on the bag-of-visual-words (BOVW) model is one of the most successful ways to acquire the high-level semantic concepts. However, the BOVW model assigns local low-level features to their closest visual words in the “visual vocabulary” (the codebook obtained by k-means clustering), which discards too many useful details of the low-level features in HSR images. In this paper, a feature coding method under the Fisher kernel (FK) coding framework is introduced to extend the BOVW model by characterizing the low-level features with a gradient vector instead of the count statistics in the BOVW model, which results in a significant decrease in the codebook size and an acceleration of the codebook learning process. By considering the differences in the distributions of the ground objects in different regions of the images, local FK (LFK) is proposed for the HSR image scene classification method. The experimental results show that the proposed scene classification methods under the FK coding framework can greatly reduce the computational cost, and can obtain a better scene classification accuracy than the methods based on the traditional BOVW model.

In recent years, transfer learning with pretrained convolutional networks (CNets) has been successfully applied to land-use classification with high spatial resolution (HSR) imagery. The commonly used transfer CNets partially use the feature descriptor part of the pretained CNets, and replace the classifier part of the pretrained CNets in the old task with a new one. This causes the separation and asynchrony between the feature descriptor part and the classifier part of the transferred CNets during the learning process, which reduces the effectiveness of the training process. To overcome this weakness, a transfer learning method with fully pretrained CNets is proposed in this letter for the land-use classification of HSR images. In the proposed method, a multilayer perceptron (MLP) classifier is quickly pretrained using the high-level features extracted by the feature descriptor of the pretrained CNets. Fully pretrained CNets can be generated by concatenating the feature descriptor of the pretrained CNets and the pretained MLP. Because both the feature descriptor and the classifier are pretrained, the separation and asynchrony between the two parts can be avoided during the training process. The final transferred CNets are then obtained by fine-tuning the fully pretrained CNets with the random cropping and mirroring strategy. The experiments show that the proposed method can accelerate the convergence of the training process with no loss of accuracy in land-use classification, and its performance is comparable to other latest methods.

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.

As a result of the large semantic gap between the low-level features and the high-level semantics, scene understanding is a challenging task for high satellite resolution images. To achieve scene understanding, we need to know the contents of the scene. However, most of the existing scene classification methods, such as the bag-of-visual-words model (BoVW), feature coding, topic models, and neural networks, can only classify the scene while ignoring the components and the semantic and spatial relations between these components. Therefore, in this paper, a bottom-up scene understanding framework based on the multi-object spatial context relationship model (MOSCRF) is proposed to combine the co-occurrence relations and position relations at the object level. In MOSCRF, the co-occurrence relation features are modeled by the fisher kernel coding of objects (oFK), while the position relation features are represented by the multi-object force histogram (MOFH). The MOFH is the evolution of the force histogram between pairwise objects. The MOFH not only has the property of being invariant to rotation and mirroring, but also acquires the spatial distribution of the scene by calculating the acting force between multiple land-cover objects. Due to the utilization of the prior knowledge of the objects’ information, MOSCRF can explain the objects and their relations to allow understanding of the scene. The experiments confirm that the proposed MOSCRF can reflect the layout mode of the scene both semantically and spatially, with a higher precision than the traditional methods.

Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension.