This paper presents some preliminary results using Landsat and Worldview images for change detection. The studied area had some significant changes such as construction of buildings between May 2014 and October 2015. We investigated several simple, practical, and effective approaches to change detection. For Landsat images, we first performed pansharpening to enhance the resolution to 15 meters. We then performed a chronochrome covariance equalization between two images. The residual between the two equalized images was then analyzed using several simple algorithms such as direct subtraction and global Reed-Xiaoli (GRX) detector. Experimental results using actual Landsat images clearly demonstrated that the proposed methods are effective. For Worldview images, we used pansharpened images with only four bands for change detection. The performance of the aforementioned algorithms is comparable to that of a commercial package developed by Digital Globe.
Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled to generate the compressive measurements. Even in such special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using optical videos in the SENSIAC database demonstrated the efficacy of the proposed approach.
Object tracking and classification in infrared videos are challenging due to large variations in illumination, target sizes, and target orientations. Moreover, if the infrared videos only generate compressive measurements, then it will be even more difficult to perform target tracking and classification directly in the compressive measurement domain, as many conventional trackers and classifiers can only handle reconstructed frames from compressive measurements. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one special type of compressive measurement using pixel subsampling. That is, the original pixels in the video frames are randomly subsampled. Even in such special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.
Bayer pattern is a low cost approach to generating RGB images in commercial digital cameras. In NASA's mast camera (Mastcams) onboard the Mars rover Curiosity, Bayer pattern has also been used in capturing the RGB bands. It is well known that debayering (also known as demosaicing) introduces color and zipper artifacts. Currently, NASA is using a demosaicing algorithm developed in early 2000’s. It is probably the right time to assess some state-of-the-art algorithms and recommend a more recent and powerful approach to NASA for its future missions. In this paper, we present results of a comparative study on the use of conventional and deep learning algorithms for demosaicing Mastcam images. Due to lack of ground truth, subjective evaluation has been used in our study.
KEYWORDS: Image compression, Error analysis, Imaging systems, RGB color model, Video compression, Multispectral imaging, Cameras, Principal component analysis, Mars, Video
We present a high performance image compression framework for Mastcam images in the Mars rover Curiosity. First, we aim at achieving perceptually lossless compression. Four well-known image codecs in the literature have been evaluated and the performance was assessed using four well-known performance metrics. Second, we investigated the impact of error concealment algorithms for handling corrupted pixels due to transmission errors in communication channels. Extensive experiments using actual Mastcam images have been performed to demonstrate the proposed framework.
Pansharpened Landsat images have 15 m spatial resolution with 16-day revisit periods. On the other hand, Worldview images have 0.5 m resolution after pansharpening but the revisit times are uncertain. We present some preliminary results for a challenging image fusion problem that fuses Landsat and Worldview (WV) images to yield a high temporal resolution image sequence at the same spatial resolution of WV images. Since the spatial resolution between Landsat and Worldview is 30 to 1, our preliminary results are mixed in that the objective performance metrics such as peak signal-to-noise ratio (PSNR), correlation coefficient (CC), etc. sometimes showed good fusion performance, but at other times showed poor results. This indicates that more fusion research is still needed in this niche application.
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.
In the 2015 NASA ROSES solicitation, NASA has expressed strong interest in improving the accuracy of Mars surface characterization using satellite images. Thermal Emission Imaging System (THEMIS), an imager with a spatial resolution of 100 meters, has 10 infrared bands between 6 and 15 micrometers. Thermal Emission Spectrometer (TES), an imager with a spatial resolution of 3 km, has 143 bands between 5 and 50 micrometers. While both imagers have a variety of applications, it would be ideal to generate high-spatial and high-spectral resolution data products by fusing their respective outputs. We present a novel approach to fusing THEMIS and TES satellite images, aiming to improve orbital characterization of Mars’ surface. First, the THEMIS bands must undergo atmospheric compensation (AC) due to the presence of dust, ice, carbon dioxide, etc. A systematic AC procedure using elevation information and spectrally uniform pixels has been developed and implemented. Second, a set of proven pan-sharpening algorithms has been applied to fuse the two sets of images. The pan-sharpened images have the spatial resolution of THEMIS images and the spectral resolution of TES images. The results of extensive experiments using THEMIS and TES data collected near the Syrtis Major region (one of the final 3 candidate landing sites for the Mars 2020 rover) clearly demonstrate the feasibility of the proposed approach.
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