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Abstract-A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification and it is validated on a well-known dataset. The hypothesis that simultaneous multiscale views can improve compositionbased inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network. This allows the classifier to obtain problemspecific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trades optimality for generality. A heuristic approach to the optimization of the deep convolutional neural network hyperparameters is used, based on empirical performance evidence. It is shown that a single deep convolutional neural network can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced dataset where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.Index Terms-Neural network applications, neural network architecture, feature extraction, urban areas, remote sensing.

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Abstract-A method for detecting land cover change using NDVI time series data derived from 500m MODIS satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time series of a 3x3 grid of MODIS pixels. The NDVI time series for each of these pixels was modeled as a triply (mean, phase and amplitude) modulated cosine function, and an Extended Kalman Filter was used to estimate the parameters of the modulated cosine function through time. A spatial comparison between the center pixel of the the 3x3 grid and each of its neighboring pixel's mean and amplitude parameter sequence was done to calculate a change metric which yields a change or no-change decision after thresholding. Although the development of new settlements is the most prevalent form of land cover change in South Africa, it is rarely mapped and known examples amounts to a limited number of changed MODIS pixels. Therefore simulated change data was generated and used for preliminary optimization of the change detection method. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 89% change detection accuracy, while a traditional annual NDVI differencing method could only achieve a 63% change detection accuracy.

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