In the field of remote sensing, it is common to have image data which can be considered in some way to be incomplete. This may relate to missing information caused by sensor failures, cloud cover or partially overlapping data acquisitions. In each of these cases it is of interest to consider how best this data can be completed. Whereas previous work has employed techniques such as low-rank tensor completion to tackle this problem, we present a graph-based propagation algorithm which diffuses entries around the incomplete image tensors. We show this approach is robust in even extreme circumstances for which large regions of image data are missing and compare the quality of our completions against the state of the art. In addition to improved performance as measured by reduced errors versus ground truth in experiments we also provide a comparison of our method’s efficiency against benchmark methods and show that the approach is scalable as well as robust.
Remote sensing images with high-spatial and high-temporal (HSHT) resolution are difficult to be consistently achieved with a single polar orbit satellite because of the trade-off between spatial and temporal resolution. However, blending algorithms have been developed to synthesize HSHT images from low-spatial but high-temporal resolution images and high-spatial but low-temporal resolution images. One example is a widely used weight-function-based method known as the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Similar-pixels play a key role in the prediction accuracy of this model, and their identification is influenced by the method used, the number of classes, and the moving window size. However, influence of these parameters on the final prediction accuracy has not been thoroughly examined. Therefore, this study assesses the accuracy of ESTARFM when similar-pixels are identified by two separate methods using different numbers of classes and moving window sizes. The results indicated that the class-image-based method outperformed the threshold-based method in most cases and the increase of moving window size generally improved the accuracy for both methods. However, the improvement was minor and accuracy degraded in some cases if the moving window was too large. In addition, the ESTARFM program was optimized in this study, and its computational efficiency was improved compared with previous studies.
KEYWORDS: Detection and tracking algorithms, Mining, Tolerancing, Data analysis, Data modeling, Feature selection, Feature extraction, Radar, Computer architecture, Analytical research
In order to leverage computational complexity and avoid information losses, “big data” analysis requires a new class of algorithms and methods to be designed and implemented. In this sense, information theory-based techniques can play a key role to effectively unveil change and anomaly patterns within big data sets. A framework that aims at detecting the anomaly patterns of a given dataset is introduced. The proposed method, namely PROMODE, relies on a representation of the given dataset performed by means of undirected bipartite graphs. Then the anomalies are searched and detected by progressively spanning the graph. The proposed architecture delivers a computational load that is less than that carried by typical frameworks in literature, so that PROMODE can be considered as a valid algorithm for efficient detection of change patterns in remotely sensed big data.
Reducing the size of the data on-ground with no information loss represents a strong challenge for the scientific community, since Earth observation (EO) data volumes have strongly and steadily grown during the last 10 years and the need for more efficient compression methods is growing stronger. High-accuracy processing methods employed for EO data understanding and quantifying may result in effective methods for image compression. We propose to use a robust framework of endmember extraction and nonlinear modeling for the on-ground compression of EO data records, where the distribution of the mixture coefficient is exploited to enhance the compression gain while providing high-accuracy reconstruction. Experimental results over real EO datasets show the actual power of the proposed approach.
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