With substantial attention being received from industry and academia over last decade, salient object detection has become an important fundamental research in computer vision. The solution of salient object detection will be helpful to make breakthroughs in various visual tasks. Although various works have achieved remarkable success for saliency detection tasks in visible light scenes, there still remain a challenging issue on how to extract salient objects with clear boundary and accurate internal structure in weak light images with low signal-to-noise ratios and limited effective information. For that fuzzy boundary and incomplete internal structure cause low accuracy of salient object detection in weak light scenes, an Ant Colony Optimization (ACO) algorithm based saliency detection framework was proposed. Firstly, the input image was transformed into an undirected graph with different nodes by multi-scale superpixel segmentation. Secondly, the optimal feature selection strategy was adopted to capture the useful information contained in the salient object and eliminate the redundant noise information from weak light image with low contrast. Then, the spatial contrast strategy was introduced to explore the global saliency cues with relatively high contrast in the weak light image. To acquire more accurate saliency estimation at low signal-to-noise ratio, the ACO algorithm was used to optimize the saliency map. Through the experiments on three public datasets (MSRA, CSSD and PASCAL-S) and the Nighttime Image (NI) dataset, it can be seen that the Area Under the Curve (AUC) value of the proposed model reached 87.47%, 84.27% and 81.58% on three public datasets respectively, and the AUC value of the model was increased by 2.17 percentage points compared to that of the Low Rank Matrix Recovery (LR) model (which ranked the second) on the NI dataset. The results demonstrate that the proposed model has the detection effect with more accurate structure and clearer boundary compared to 11 mainstream saliency detection models and effectively suppresses the interference of weak light scenes on the detection performance of salient objects.

The Convolutional Neural Network (CNN) treats the potential object information and background information equally in the input image. However, there are many small objects and complex background in remote sensing scene images. To solve the problem above, a scale-attention network was proposed based on attention mechanism and multi-scale feature transformation. Firstly, a fast and effective attention module was developed, and the attention map was generated based on optimal feature selection. Then, with the attention map embedded, the multi-scale feature fusion layer added and the fully connected layer redesigned on the basis of ResNet50 network, a scale attention network was proposed. Secondly, the pre-training model was used to initialize the scale-attention network, and the training set was employed for the fine-tuning of the network. Finally, the fine-tuned scale-attention network was used to realize the classification prediction of test set. The classification accuracy of the proposed method on the AID scene dataset is 95.72%, which is 2.62 percentage points higher than that of ArcNet. On the NWPU-RESISC scene dataset, this method achieves classification accuracy of 92.25%, 0.95 percentage points higher than that of IORN （Improved Oriented Response Network）. The experimental results demonstrate that the proposed method is able to improve the classification accuracy of remote sensing image scenes.