Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

TL;DR

This paper introduces DAFNet, a novel deep learning model with a global context-aware attention mechanism for improved salient object detection in optical remote sensing images, supported by a new large-scale dataset.

Contribution

The paper proposes a new Dense Attention Fluid Network with a global context-aware attention module and introduces the largest annotated dataset for this task.

Findings

DAFNet outperforms existing methods on the new dataset

The GCA module effectively captures long-range semantic relationships

The cascaded pyramid attention refines scale variation handling

Abstract

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. https://github.com/rmcong/DAFNet_TIP20