Hongqi Wang

Bio: Hongqi Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Object detection & Feature extraction. The author has an hindex of 21, co-authored 63 publications receiving 1661 citations. Previous affiliations of Hongqi Wang include Curtin University.

Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

Abstract: In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a $k$ -nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.

Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model

Abstract: Automatic detection for targets with complex shape in high-resolution remote sensing images is a challenging task. In this letter, we propose a new detection framework based on spatial sparse coding bag-of-words (BOW) (SSCBOW) model to solve this problem. Specifically, after selecting a processing unit by the sliding window and extracting features, a new spatial mapping strategy is used to encode the geometric information, which not only represents the relative position of the parts of a target but also has the ability to handle rotation variations. Moreover, instead of K-means for visual-word encoding in the traditional BOW model, sparse coding is introduced to achieve a much lower reconstruction error. Finally, the SSCBOW representation is combined with linear support vector machine for target detection. The experimental results demonstrate the precision and robustness of our detection method based on the SSCBOW model.

Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks

Abstract: Object detection has been one of the hottest issues in the field of remote sensing image analysis. In this letter, an efficient object detection framework is proposed, which combines the strength of the unsupervised feature learning of deep belief networks (DBNs) and visual saliency. In particular, we propose an efficient coarse object locating method based on a saliency mechanism. The method could avoid an exhaustive search across the image and generate a small number of bounding boxes, which can locate the object quickly and precisely. After that, the trained DBN is used for feature extraction and classification on subimages. The feature learning of the DBN is operated by pretraining each layer of restricted Boltzmann machines (RBMs) using the general layerwise training algorithm. An unsupervised blockwise pretraining strategy is introduced to train the first layer of RBMs, which combines the raw pixels with a saliency map as inputs. This makes an RBM generate local and edge filters. The precise edge position information and pixel value information are more efficient to build a good model of images. Comparative experiments are conducted on the data set acquired by QuickBird with a 60-cm resolution. The results demonstrate the accuracy and efficiency of our method.

A New Method on Inshore Ship Detection in High-Resolution Satellite Images Using Shape and Context Information

Abstract: In this letter, we present a new method to detect inshore ships using shape and context information. We first propose a new energy function based on an active contour model to segment water and land and minimize it with an iterative global optimization method. The proposed energy performs well on the different intensity distributions between water and land and produces a result that can be well used in shape and context analyses. In the segmented image, ships are detected with successive shape analysis, including shape analysis in the localization of ship head and region growing in computing the width and length of ship. Finally, to locate ships accurately and remove the false alarms, we unify them with a binary linear programming problem by utilizing the context information. Experiments on QuickBird images show the robustness and precision of our method.

Deep Learning for Computer Vision: A Brief Review.

Abstract: Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.