The Earth’s surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries.
Radar satellite images could be used to produce digital elevation model (DEM) of certain areas by processing a couple of images, covering the same area, obtained at two different angles. In this study, the DEM generated from the Canadian RADARSAT stereoscopic data for a north western area of the Gulf of Suez, Egypt, is compared to the DEM generated from the topographic contour maps, scale 1:50,000. An evaluation and assessment of the results were conducted. The study shows that the DEM derived from RADARSAT data has a high precision as compared to the one generated from the topographic maps. It is also accurate enough to provide information where other sources of digital elevation are not available.
A variety of techniques exist for change detection of multitemporal remotely sensed satellite data. The Intensity- Hue-Saturation (IHS) color space is very useful for image processing because it separates the color information in ways that correspond to the human visual system's response. In this study, a novel approach, emphasizing the use of the hue component of the IHS transformations of Landsat data, is proposed and examined for multitemporal change detection. Two Landsat Thematic Mapper (TM) scenes acquired on 1987 and 1997 covering the western part of El-Fayoum area and El- Rayan lakes in Egypt have been processed (geometrically corrected and radiometrically balanced) and transformed to the IHS space. The results of using the hue component in detecting the changes are very promising. A number of changed areas including water and agriculture land were successfully detected. The used color theme print, which display the spatial pattern of change in map form, was of great significance in interpreting the environmental changes and the statistical estimation of these changes has been carried out as well.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.