The Mastcam multispectral imagers onboard the Mars rover Curiosity have been collecting data since 2012. There are two imagers. The left imager has wide field of view, but three times lower resolution than that of the right, which is just the opposite. Left and right images can be combined to generate stereo images. However, the resolution of the stereo images using conventional ways is at the same resolution of the left. Ideally, it will be more interesting to science fans and rover operators if one can generate stereo images with the same resolution of the right imager, as the resolution will be three times better. Recently, we have developed some algorithms that can fuse left and right images to create left images with the same resolution of the right. Consequently, high resolution stereo images can be generated. Moreover, disparity image can also be generated. In this paper, we will summarize the development of a data processing pipeline that takes left and right Mastcam images from the Planetary Data System (PDS) archive, performs pansharpening to enhance the left images with help from the right images, generates high resolution stereo images, disparity maps, and saves the processed images back into the PDS archive. The details of the workflow will be described. For example, image alignment algorithm, the pansharpening algorithm, stereo image formation algorithms, and disparity map generation algorithms will be summarized. Some demonstration examples will be given as well.
Ground object detection is important for many civilian applications. Counting the number of cars in parking lots can provide very useful information to shop owners. Tent detection and counting can help humanitarian agencies to assess and plan logistics to help refugees. In this paper, we present some preliminary results on ground object detection using high resolution Worldview images. Our approach is a simple and semi-automated approach. A user first needs to manually select some object signatures from a given image and builds a signature library. Then we use spectral angle mapper (SAM) to automatically search for objects. Finally, all the objects are counted for statistical data collection. We have applied our approach to tent detection for a refugee camp near the Syrian-Jordan border. Both multispectral Worldview images with eight bands at 2 m resolution and pansharpened images with four bands at 0.5 m resolution were used. Moreover, synthetic hyperspectral (HS) images derived from the above multispectral (MS) images were also used for object detection. Receiver operating characteristics (ROC) curves as well as detection maps were used in all of our studies.
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