The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following gescience task when developing inpainting methods. This paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at:https://github.com/HMS97/Task-driven-Inpainting.

Salient Object Detection (SOD) plays an important role in many image-related multimedia applications. Although there are many existing research works about the salient object detection in traditional RGB (visible-light spectrum) images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we mainly model this research problem as a deep learning based domain adaptation from the traditional RGB image data (source domain) to the multi-spectral data (target domain), and an adversarial deep domain adaptation model is proposed. We first collect and will publicize a large multi-spectral dataset, RGBN-SOD dataset, including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. Intensive experimental results show the effectiveness and accuracy of the proposed deep domain adaptation for the multi-spectral SOD. Besides, due to the absence of research on the field of multi-spectral co-saliency detection, we also collect 200 synchronized RGB and NIR image pairs in addition to explore the multi-spectral co-saliency detection.

Video anomaly detection is of great significance due to its wide applications in video surveillance. Recently, there is a trend of using a video prediction framework to tackle this problem. This kind of methods detects anomalies according to the difference between a predicted frame and its ground truth. However, existing prediction methods lack the consideration of multi-scale temporal constraints on generating future frames. Therefore, based on the prediction framework, this paper proposes a temporal enhanced anomaly detection approach, which designs a generative adversarial network with dual discriminator (frame discriminator and sequence discriminator) to predict future frames. In order to obtain more realistic predictions, other than commonly used spatial constraints and the adversarial penalty from the frame discriminator, we also consider both short-range and long-range motions to impose constraints. Specifically, for short-range motion modeling, we utilize the optical flow loss to ensure temporal continuity over two adjacent frames, while for long-range motion modeling, we design a sequence discriminator to identify fake contained sequences from real sequences, making frames more consistent with their previous consecutive frames. Experiments on three datasets, UCSD Ped1, UCSD Ped2 and Avenue, demonstrate the effectiveness of our method in terms of various evaluation criteria for video anomaly detection.

Although there are many existing research works about the salient object detection (SOD) in RGB images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we first collect and will publicize a large multi-spectral dataset including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. We model this research problem as an adversarial domain adaptation from the existing RGB image dataset (source domain) to the collected multi-spectral dataset (target domain). Experimental results show the effectiveness and accuracy of the proposed adversarial domain adaptation for the multi-spectral SOD.

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the RS techniques, a large number of RS scene images are available. As manually labeling large-scale RS scene images is both labor and time consuming, when a new unlabeled data set is obtained, how to use the existing labeled data sets to classify the new unlabeled images is an important research direction. Different RS scene image data sets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions, and image scales, so the distribution discrepancy exists among different image data sets. As a result, simply applying convolutional neural networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain adaptation (DA) can be helpful to solve this problem. In this letter, we design a subspace alignment (SA) and CNN-based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Fine-tuning the modified CNN model with the added SA layer makes the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public data sets show that adding the SA layer into CNN improves the scene classification on the target domain.