Scene Classification from Synthetic Aperture Radar Images Using Generalized Compact Channel-Boosted High-Order Orderless Pooling Network

The convolutional neural network (CNN) has achieved great success in the field of scene classification. Nevertheless, strong spatial information in CNN and irregular repetitive patterns in synthetic aperture radar (SAR) images make the feature descriptors less discriminative for scene classification. Aiming at providing more discriminative feature representations for SAR scene classification, a generalized compact channel-boosted high-order orderless pooling network (GCCH) is proposed. The GCCH network includes four parts, namely the standard convolution layer, second-order generalized layer, squeeze and excitation block, and the compact high-order generalized orderless pooling layer. Here, all of the layers are trained by back-propagation, and the parameters enable end-to-end optimization. First of all, the second-order orderless feature representation is acquired by the parameterized locality constrained affine subspace coding (LASC) in the second-order generalized layer, which cascades the first and second-order orderless feature descriptors of the output of the standard convolution layer. Subsequently, the squeeze and excitation block is employed to learn the channel information of parameterized LASC statistic representation by explicitly modelling interdependencies between channels. Lastly, the compact high-order orderless feature descriptors can be learned by the kernelled outer product automatically, which enables low-dimensional but highly discriminative feature descriptors. For validation and comparison, we conducted extensive experiments into the SAR scene classification dataset from TerraSAR-X images. Experimental results illustrate that the GCCH network achieves more competitive performance than the state-of-art network in the SAR image scene classification task

Deep Vision in Optical Imagery: From Perception to Reasoning

Deep learning has achieved extraordinary success in a wide range of tasks in computer vision field over the past years. Remote sensing data present different properties as compared to natural images/videos, due to their unique imaging technique, shooting angle, etc. For instance, hyperspectral images usually have hundreds of spectral bands, offering additional information, and the size of objects (e.g., vehicles) in remote sensing images is quite limited, which brings challenges for detection or segmentation tasks. This thesis focuses on two kinds of remote sensing data, namely hyper/multi-spectral and high-resolution images, and explores several methods to try to find answers to the following questions: - In comparison with natural images or videos in computer vision, the unique asset of hyper/multi-spectral data is their rich spectral information. But what this “additional” information brings for learning a network? And how do we take full advantage of these spectral bands? - Remote sensing images at high resolution have pretty different characteristics, bringing challenges for several tasks, for example, small object segmentation. Can we devise tailored networks for such tasks? - Deep networks have produced stunning results in a variety of perception tasks, e.g., image classification, object detection, and semantic segmentation. While the capacity to reason about relations over space is vital for intelligent species. Can a network/module with the capacity of reasoning benefit to parsing remote sensing data? To this end, a couple of networks are devised to figure out what a network learns from hyperspectral images and how to efficiently use spectral bands. In addition, a multi-task learning network is investigated for the instance segmentation of vehicles from aerial images and videos. Finally, relational reasoning modules are designed to improve semantic segmentation of aerial images