Satellite image data fusion is a topic of interest in many areas including environmental monitoring, emergency response, and defense. Typically any single satellite sensor cannot provide all of the benefits offered by a combination of different sensors (e.g., high-spatial but low spectral resolution vs. low-spatial but high spectral, optical vs. SAR). Given the respective strengths and weaknesses of the different types of image data, it is beneficial to fuse many types of image data to extract as much information as possible from the data.
Our work focuses on the fusion of multi-sensor image data into a unified representation that incorporates the potential strengths of a sensor in order to minimize classification error. Of particular interest is the fusion of optical and synthetic aperture radar (SAR) images into a single, multispectral image of the best possible spatial resolution. We explore various methods to optimally fuse these images and evaluate the quality of the image fusion by using K-means clustering to categorize regions in the fused images and comparing the accuracies of the resulting categorization maps.
The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success.
In this work, we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a "mixture of experts" in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately
11.5% higher change detection accuracy than an individual classifier.