Suchergebnisse

The factors affecting the relationship between China and India can be divided into three categories: structural factors, hard factors, and soft factors. The structural factors are mainly geopolitical factors determined by national strength, geographical features and international status. Hard factors mainly include border conflicts, Tibetan issues, China–Pakistan relations and water disputes, which are difficult to solve and highly sensitive. Soft factors include a trade imbalance, visa issues, different notions of history, strategic differences, and the relationship between the two countries on the international stage. These three kinds of factors are differentiated. Their importance and influence on China–India relations are also changing. Geopolitical factors have begun to play more important role in the bilateral relationship of the two rising countries in the past few years, leading to their strategic competition. This competition has grown despite the fact that the two countries have not yet achieved a status as leaders of world politics. This premature strategic competition will hinder the development of the two countries and will make the "Asian century" hard to realize. For the security and interests of both nations and Asia as a whole, China and India must establish a more stable geopolitical relationship, promote bilateral cooperation in the field of hard and soft factors, and find opportunities for cooperation in new areas and spaces. Finally, China and India need to build a new type of power relations.

The China-US relationship is a multidimensional complex one involving both traditional and nontraditional security issues. However, nontraditional security issues (NTS) have become paramount in reshaping Sino-US relations, though there is no absolute boundary between NTS and traditional security. With both traditional security issues and NTS issues being solved according to the involved nations' prior experience in dealing with traditional security matters, it stands to reason that there is a very fine line, if that, between NTS and traditional security and that they are not necessarily mutually exclusive. Although the energy threat is more likely to be considered a traditional security matter than concerns such as the terrorism threat or climate change issue, the energy threat actually contains some NTS characteristics, like how to get full use of natural resources and the relevance with climate change. This article, to some extent, explains the dynamic between traditional and nontraditional security through the case study of China-US relations. (J Contemp China/GIGA)

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the "V" ship head model and the "||" ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the "V" ship head model and the "||" ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.