In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

Information about the location and extent of informal settlements is necessary to guide decision making and resource allocation for their upgrading. Very high resolution (VHR) satellite images can provide this useful information, however, different urban settlement types are hard to be automatically discriminated and extracted from VHR imagery, because of their abstract semantic class definition. State-of-the-art classification techniques rely on hand-engineering spatial-contextual features to improve the classification results of pixel-based methods. In this paper, we propose to use convolutional neural networks (CNNs) for learning discriminative spatial features, and perform automatic detection of informal settlements. The experimental analysis is carried out on a QuickBird image acquired over Dar es Salaam, Tanzania. The proposed technique is compared against support vector machines (SVMs) using texture features extracted from grey level co-occurrence matrix (GLCM) and local binary patterns (LBP), which result in accuracies of 86.65% and 90.48%, respectively. CNN leads to better classification, resulting in an overall accuracy of 91.71%. A sensitivity analysis shows that deeper networks result in higher accuracies when large training sets are used. The study concludes that training CNN in an end-to-end fashion can automatically learn spatial features from the data that are capable of discriminating complex urban land use classes.

Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.