Search

Glaciers are the source of fresh water that have obvious response on climate change. It is crucial and meaningful to monitor glacier area. In this paper, a glacier detection method based on features derived from low-resolution optical data, thermal data and TerraSAR-X images is proposed. The Land Surface Temperature (LST) was firstly obtained by Convolutional Neural Network (CNN). Combined with the velocity information, a low-resolution binary mask was derived for the supervised classification of SAR imagery. Afterwards, a set of suitable features was derived from the SAR intensity image, such as texture information generated based on the gray level co-occurrence matrix (GLCM), and intensity values. With these features above, the glaciers were classified by Random Forest (RF) to distinguish the glacier from the non-glacier areas. Compared to the unsupervised classification only using SAR data, the glacier detection method proposed in this paper achieved a better performance with the overall classification accuracy of 90.88%.

Many image super-resolution algorithms can be formulated in a mapping framework based on the natural image prior. Generally, the mapping function with free parameters is learned by minimizing the reconstruction mapping error. In this paper, we obtain a considerable image super-resolution algorithm which gains in accuracy and speed by combining joint mapping learning with fast approximations of sparse coding. A novel “dictionary” training method for single image super-resolution based on feed-forward neural network is proposed. The training algorithm alternates between solving sparse coding problem and learning joint mapping relation problem. The learning process enforces that the sparse coding of a low-resolution image patch can be regarded as the shared latent coordinates for reconstructing its underlying high-resolution image patch with the image high-resolution dictionary. Experiments validate that our learning method shows excellent results both quantitatively and qualitatively.

Fusarium wilt of banana, a lethal fungal disease caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense (Foc), has become a plague and serious threat in banana-producing regions of the world. Since spread in china, this disease had caused huge conomic losses to the banana industry. In this chapter, we describe the invasion situation and epidemiology of banana disease in china, and discuss the pathogenic mechanism on virulent toxin and virulence genes. Further, we summarize the research progress of molecular detection of pathogens. Finally, we review the research progress on management strategies, including strict quarantine system, breeding and cultivation of disease-resistant varieties, chemical pesticide control, and biological control and cultural control.