Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

In remote sensing data exploitation, the spectral mixture analysis technique is generally used to detect the land cover materials and their corresponding proportions present in the observed scene. Traditionally, a fixed endmember spectral signature for each land cover material is used to perform the unmixing task. In the literature, some scholars have proposed performing the unmixing by taking the spectral variability into consideration. Among these spectral-variability-based unmixing approaches, multiple-endmember spectral mixture analysis (MESMA) is probably the most widely used method. However, when the number of land cover materials is large, the computational load of the MESMA method could be very heavy. In this paper, a sparse multiple-endmember spectral mixture model (SMESMM) is proposed to handle this problem. This model treats the spectral mixture procedure as a linear block sparse inverse problem. The SMESMM is first solved using a block sparse algorithm to obtain an initial block sparse solution. Then, MESMA is used to resolve the mixed pixel using the selected land cover materials, which correspond to the nonzero blocks in the solution obtained in the first step. The block sparse solution obtained in the first step can help to determine how many and which land cover materials are involved in the considered mixed pixel. This can largely decrease the number of possible candidate models for the MESMA method when the number of land cover materials is large. Experimental results on simulated and real hyperspectral data demonstrate the efficacy of the proposed method.

Traditional indoor navigation algorithms generally only consider the geometrical information of indoor space. However, the environmental information and semantic parameters of a fire are also important for evacuation routing in the case of a fire. It is difficult for traditional indoor navigation algorithms to dynamically find an indoor path when a fire develops. To address this problem, we developed a multi-semantic constrained three-dimensional (3D) indoor fire evacuation routing method that considers multi-dimensional indoor fire scene-related semantics, such as path accessibility, path recognition degree, and fire parameters. Our method enhances the navigation semantics of indoor space by extending the fire-related components of indoor model based on IndoorGML and integrating location semantics of IndoorLocationGML. We also propose quantifiable indoor fire-oriented routing semantics and establish a navigation cost function that evaluates semantic changes during a fire. We designed an indoor routing algorithm with multiple semantic constraints based on the A* algorithm. The indoor routing results were analyzed and compared in simulation experiments. The experimental results showed that the proposed model can remove unusable nodes and edges from the obtained navigation path and provides a safer and more effective evacuation route than traditional algorithms.