Over the past two decades, medical imaging and diagnostic techniques have gained
immense attraction due to the rapid development in computing, internet, data storage and …

Image Fusion is a process of combining two or more images into a single image which is
more informative and hence more useful from an interpretation point of view. With the rapid …

Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …

Synthetic aperture radar (SAR) images are often contaminated by a multiplicative noise
known as speckle. Speckle makes the processing and interpretation of SAR images difficult …

Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …

In this paper we investigate the use of discriminative model learning through Convolutional
Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning …

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects
synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three …

Ternary change detection aims to detect changes and group the changes into positive
change and negative change. It is of great significance in the joint interpretation of spatial …

Speckle noise is an inherent problem in coherent imaging systems such as synthetic
aperture radar. It creates strong intensity fluctuations and hampers the analysis of images …

In this paper, to break the limit of the traditional linear models for synthetic aperture radar
(SAR) image despeckling, we propose a novel deep learning approach by learning a non …