Residential-area segmentation is one of the most fundamental tasks in the field of remote sensing. Recently, fully supervised convolutional neural network (CNN)-based methods have shown superiority in the field of semantic segmentation. However, a serious problem for those CNN-based methods is that pixel-level annotations are expensive and laborious. In this study, a novel hierarchical weakly supervised learning (HWSL) method is proposed to realize pixel-level semantic segmentation in remote sensing images. First, a weakly supervised hierarchical saliency analysis is proposed to capture a sequence of class-specific hierarchical saliency maps by computing the gradient maps with respect to the middle layers of the CNN. Then, superpixels and low-rank matrix recovery are introduced to highlight the common salient areas and fuse class-specific saliency maps with adaptive weights. Finally, a subtraction operation between class-specific saliency maps is conducted to generate hierarchical residual saliency maps and fulfill residential-area segmentation. Comprehensive evaluations with two remote sensing data sets and comparison with seven methods validate the superiority of the proposed HWSL model.

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.

Image superresolution (SR) techniques have widely been used to satisfy the increasing resolution demands of many advanced applications in the remote sensing domain. However, most of the existing SR models are directly operated on the whole image without considering the deviations and different objective requirements of different regions in optical remote sensing images, which are not sensible enough. To fill this gap, we propose a novel saliency-oriented adaptive SR strategy motived by the visual attention mechanism. The key idea of this letter is employing diverse treatments on different regions according to their unique requirements. For instance, the reconstruction quality of regions of interest (ROIs) should be as fine as possible. First, we employ a saliency detection strategy based on the edge-enhancement discrete wavelet transform to generate a saliency map, which clearly demonstrates the distribution of ROIs. Then, with regard to these areas, a new SR strategy is applied to get a better performance, where the training process is upgraded with the feature optimization process. In addition, the rest regions also receive the standard A+ to improve the quality there. Finally, all restored high-resolution (HR) patches are fused together as the desired reconstructed HR image. The comparative experiments validate the effectiveness of our scheme.