Zhengxia Zou

Ship Detection in Spaceborne Optical Image With SVD Networks

Automatic ship detection on spaceborne optical images is a challenging task, which has attracted wide attention due to its extensive potential applications in maritime security and traffic control. Although some optical image ship detection methods have been proposed in recent years, there are still three obstacles in this task: 1) the inference of clouds and strong waves; 2) difficulties in detecting both inshore and offshore ships; and 3) high computational expenses. In this paper, we propose a novel ship detection method called SVD Networks (SVDNet), which is fast, robust, and structurally compact. SVDNet is designed based on the recent popular convolutional neural networks and the singular value decompensation algorithm. It provides a simple but efficient way to adaptively learn features from remote sensing images. We evaluate our method on some spaceborne optical images of GaoFen-1 and Venezuelan Remote Sensing Satellites. The experimental results demonstrate that our method achieves high detection robustness and a desirable time performance in response to all of the above three problems.

Hierarchical Suppression Method for Hyperspectral Target Detection

Target detection is an important application in the hyperspectral image processing field, and several detection algorithms have been proposed in the past decades. Some traditional detectors are built based on the statistical information of the target and background spectra, and their performances tend to be affected by the spectral quality. Some previous methods cope with this problem by refining the target spectra to make the detector robust. In this paper, instead of doing similar to this, we propose a new hierarchical method to suppress the backgrounds while preserving the target spectra, with the purpose of boosting the performance of traditional hyperspectral target detector. The proposed method consists of different layers of classical constrained energy minimization (CEM) detectors. In each layer of detection, the CEMs output of each spectrum is transformed by a nonlinear suppression function and then considered as a coefficient to impose on this spectrum for the next round of iteration. To our knowledge, such hierarchical structure is proposed for the first time. Theoretically, we prove the convergence of the proposed algorithm, and we also give a theoretical explanation on why we can obtain the gradually increasing detection performance through the hierarchical suppression process. Experimental results on two real hyperspectral images and one synthetic image suggest that our method significantly improves the performance of the original CEM detection algorithm and also outperforms other classical and recently proposed hyperspectral target detection algorithms.

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

In current remote sensing literature, the problems of sea-land segmentation and ship detection (including in-dock ships) are investigated separately despite the high correlation between them. This inhibits joint optimization and makes the implementation of the methods highly complicated. In this paper, we propose a novel fully convolutional network to accomplish the two tasks simultaneously, in a semantic labeling fashion, i.e., to label every pixel of the image into 3 classes, sea, land and ships. A multi-scale structure for the network is proposed to address the huge scale gap between different classes of targets, i.e., sea/land and ships. Conventional multi-scale structure utilizes shortcuts to connect low level, fine scale feature maps to high level ones to increase the network’s ability to produce finer results. In contrast, our proposed multi-scale structure focuses on increasing the receptive field of the network while maintaining the ability towards fine scale details. The multi-scale convolution network accommodates the huge scale difference between sea-land and ships and provides comprehensive features, and is able to accomplish the tasks in an end-to-end manner that is easy for implementation and feasible for joint optimization. In the network, the input forks into fine-scale and coarse-scale paths, which share the same convolution layers to minimize network parameter increase, and then are joined together to produce the final result. The experiments show that the network tackles the semantic labeling problem with improved performance.

Can a Machine Generate Humanlike Language Descriptions for a Remote Sensing Image

This paper investigates an intriguing question in the remote sensing field: “can a machine generate humanlike language descriptions for a remote sensing image?” The automatic description of a remote sensing image (namely, remote sensing image captioning) is an important but rarely studied task for artificial intelligence. It is more challenging as the description must not only capture the ground elements of different scales, but also express their attributes as well as how these elements interact with each other. Despite the difficulties, we have proposed a remote sensing image captioning framework by leveraging the techniques of the recent fast development of deep learning and fully convolutional networks. The experimental results on a set of high-resolution optical images including Google Earth images and GaoFen-2 satellite images demonstrate that the proposed method is able to generate robust and comprehensive sentence description with desirable speed performance.

Real-Time Traffic Light Detection With Adaptive Background Suppression Filter

Traffic light detection plays an important role in intelligent transportation system, and many detection methods have been proposed in recent years. However, illumination variation effect is still of its major technical problem in real urban driving environments. In this paper, we propose a novel vision-based traffic light detection method for driving vehicles, which is fast and robust under different illumination conditions. The proposed method contains two stages: the candidate extraction stage and the recognition stage. On the candidate extraction stage, we propose an adaptive background suppression algorithm to highlight the traffic light candidate regions while suppressing the undesired backgrounds. On the recognition stage, each candidate region is verified and is further classified into different traffic light semantic classes. We evaluate our method on video sequences (more than 5000 frames and labels) captured from urban streets and suburb roads in varying illumination and compared with other vision-based traffic detection approaches. The experiment shows that the proposed method can achieve a desired detection result with high quality and robustness; simultaneously, the whole detection system can meet the real-time processing requirement of about 15 fps on video sequences.

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm “random access memories (RAM).” In this paradigm, “Memories” can be interpreted as any model distribution learned from training data and “random access” means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set “LEarning, VIsion and Remote sensing laboratory (LEVIR)”, which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

Super-Resolution for Remote Sensing Images via Local–Global Combined Network

Super-resolution is an image processing technology that recovers a high-resolution image from a single or sequential low-resolution images. Recently deep convolutional neural networks (CNNs) have made a huge breakthrough in many tasks including super-resolution. In this letter, we propose a new single-image super-resolution algorithm named local–global combined networks (LGCNet) for remote sensing images based on the deep CNNs. Our LGCNet is elaborately designed with its “multifork” structure to learn multilevel representations of remote sensing images including both local details and global environmental priors. Experimental results on a public remote sensing data set (UC Merced) demonstrate an overall improvement of both accuracy and visual performance over several state-of-the-art algorithms.

Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing imagery has drawn much attention in recent years, especially with regards to the more challenging inshore ship detection. However, recent work on this subject relies heavily on hand-crafted features that require carefully tuned parameters and on complicated procedures. In this letter, we utilize a fully convolutional network (FCN) to tackle the problem of inshore ship detection and design a ship detection framework that possesses a more simplified procedure and a more robust performance. When tackling the ship detection problem with FCN, there are two major difficulties: 1) the long and thin shape of the ships and their arbitrary direction makes the objects extremely anisotropic and hard to be captured by network features and 2) ships can be closely docked side by side, which makes separating them difficult. Therefore, we implement a task partitioning model in the network, where layers at different depths are assigned different tasks. The deep layer in the network provides detection functionality and the shallow layer supplements with accurate localization. This approach mitigates the tradeoff of FCN between localization accuracy and feature representative ability, which is of importance in the detection of closely docked ships. The experiments demonstrate that this framework, with the advantages of FCN and the task partitioning model, provides robust and reliable inshore ship detection in complex contexts.

Quadratic Constrained Energy Minimization for hyperspectral target detection

In this paper, we propose a simple but effective algorithm, Quadratic Constrained Energy Minimization (QCEM) detector for hyperspectral image target detection. QCEM is a nonlinear version of classical Constrained Energy Minimization (CEM) detector, and it exploits the nonlinear characteristics of data by adding quadratic term on CEM model. Experimental results on one real hyperspectral images and one synthetic image suggest our method significantly improves the performance of the original CEM detection algorithm.

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop’s growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea–land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.