Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at
Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects. The detail refinement stage uses the proposed self-refinement module to progressively refine the predicted results under the guidance of attention and reverse attention. In addition, the hybrid loss is applied to supervise the training of the network, which can improve the performance of the model from three perspectives of pixel, region and statistics. Extensive experiments on two popular benchmarks demonstrate that AGNet achieves competitive performance compared to other state-of-the-art methods. The code will be available at
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Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors.
In literature [1], an optical remote sensing saliency detection (ORSSD) dataset with pixel-wise ground truth is built, including 600 training images and 200 testing images. This is the first publicly available dataset for the RSI SOD task, which bridges the gap between theory and practice in SOD for optical RSIs, but the amount of data is still slightly insufficient to train a deep learning based model. To enlarge the size and enrich the variety of the dataset, we extend our ORSSD dataset to a larger one named Extended ORSSD (EORSSD) dataset with 2,000 images and the corresponding pixel-wise ground truth, which includes many semantically meaningful but challenging images. Based on the ORSSD dataset, we collect additional 1,200 optical remote sensing images from the free Google Earth software, covering more complicated scene types, more challenging object attributes, and more comprehensive real-world circumstances. For clarity, the EORSSD dataset is divided into two parts, i.e., 1,400 images for training and 600 images for testing.
[1] Chongyi Li, Runmin Cong, Junhui Hou, Sanyi Zhang, Yue Qian, and Sam Kwong, Nested network with two-stream pyramid for salient object detection in optical remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 9156-9166, 2019. [----Project Page----]
Visualization of the more challenging EORSSD dataset. The first row shows the optical RSI, and the second row exhibits the corresponding ground truth. (a) Challenge in the number of salient objects. (b) Challenge in small salient objects. (c) Challenge in new scenarios. (d) Challenge in interferences from imaging. (e) Challenge in specific circumstances.
In this paper we use human visual attention mechanism to extract salient objects from remote sensing image. Different from previous methods, this method just uses bottom-up features of the input image to compute saliency map. From the saliency map we get the information about the salient object area. Guided by the saliency map, we can extract salient object from remote image. We apply this method to remote sensing image, and the experiment shows this method gets satisfying result.
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