RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT salient object detection is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection. Extensive experiments show that the proposed baseline approach outperforms the state-of-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT salient object detection on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT salient object detection.Comment: 12 pages, 10 figures https://github.com/lz118/RGBT-Salient-Object-Detectio

Achieving Sustainable Earth Futures in the Anthropocene by Including Local Communities in Critical Zone Science

Critical Zone Science (CZS) explores the deep evolution of landscapes from the base of the groundwater or the saprolite-rock interface to the top of vegetation, the zone that supports all terrestrial life. Here we propose a framework for CZS to evolve further as a discipline, building on 1st generation CZOs in natural systems and 2nd generation CZOs in human-modified systems, to incorporate human behaviour for more holistic understanding in a 3rd generation of CZOs. This concept was tested in the China-UK CZO programme (2016–2020) that established four CZOs across China on different lithologies. Beyond conventional CZO insights into soil resources, biogeochemical cycling and hydrology across scales, surveys of farmers and local government officials led to insights into human-environment interactions and key pressures affecting the socio-economic livelihoods of local farmers. These learnings combined with the CZS data identified knowledge exchange (KE) opportunities to unravel diverse factors within the Land-Water-Food Nexus, that could directly improve local livelihoods and environmental conditions, such as reduction in fertilizer use, contributing toward Sustainable Development Goals (SDGs) and environmental policies. Through two-way local KE, the local cultural context and socio-economic considerations were more readily apparent alongside the environmental rationale for policy and local action to improve the sustainability of farming practices. Seeking solutions to understand and remediate CZ degradation caused by human-decision making requires the co-design of CZS that foregrounds human behavior and the opinions of those living in human modified CZOs. We show how a new transdisciplinary CZO approach for sustainable Earth futures can improve alignment of research with the practical needs of communities in stressed environments and their governments, supporting social-ecological and planetary health research agendas and improving capacity to achieve SDGsAdditional co-authors: Xinyu Zhang, Tim A. Quine, Susan Waldron, Paul D. Hallet

Soil functions and ecosystem services research in the Chinese karst Critical Zone

Covering extensive parts of China, karst is a critically important landscape that has experienced rapid and intensive land use change and associated ecosystem degradation within only the last 50 years. In the natural state, key ecosystem services delivered by these landscapes include regulation of the hydrological cycle, nutrient cycling and supply, carbon storage in soils and biomass, biodiversity and food production. Intensification of agriculture since the late-20th century has led to a rapid deterioration in Critical Zone (CZ) state, evidenced by reduced crop production and rapid loss of soil. In many areas, an ecological ‘tipping point’ appears to have been passed as basement rock is exposed and ‘rocky desertification’ dominates. This paper reviews contemporary research of soil processes and ecosystems service delivery in Chinese karst ecosystems, with an emphasis on soil degradation and the potential for ecosystem recovery through sustainable management. It is clear that currently there is limited understanding of the geological, hydrological and ecological processes that control soil functions in these landscapes, which is critical for developing management strategies to optimise ecosystem service delivery. This knowledge gap presents a classic CZ scientific challenge because an integrated multi-disciplinary approach is essential to quantify the responses of soils in the Chinese karst CZ to extreme anthropogenic perturbation, to develop a mechanistic understanding of their resilience to environmental stressors, and thereby to inform strategies to recover and maintain sustainable soil function. © 2019 Elsevier B.V

Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

In most visual tracking tasks, the target is tracked by a bounding box given in the first frame. The complexity and redundancy of background information in the bounding box inevitably exist and affect tracking performance. To alleviate the influence of background, we propose a robust object descriptor for visual tracking in this paper. First, we decompose the bounding box into non-overlapping patches and extract the color and gradient histograms features for each patch. Second, we adopt the minimum barrier distance (MBD) to calculate patch weights. Specifically, we consider the boundary patches as the background seeds and calculate the MBD from each patch to the seed set as the weight of each patch since the weight calculated by MBD can represent the difference between each patch and the background more effectively. Finally, we impose the weight on the extracted feature to get the descriptor of each patch and then incorporate our MBD-based descriptor into the structured support vector machine algorithm for tracking. Experiments on two benchmark datasets demonstrate the effectiveness of the proposed approach

Extent to which pH and topographic factors control soil organic carbon level in dry farming cropland soils of the mountainous region of Southwest China

Soil organic carbon (SOC) in agricultural land is influenced greatly by indeterminate human activity, making it difficult to understand the spatial pattern of SOC. Soil pH and topographic conditions are key indices in the Chinese Soil Genetic Classification System (CSGCS) and manage some critical factors that control the dynamics of SOC either directly or indirectly. To identify the extent to which pH and topographic factors control SOC levels in dry farming cropland soils of the mountainous region of Southwest China, we compared the differences along topographic gradients, and analysed the contribution of different factors in determining SOC status using analysis of variance (ANOVA) and linear regression: Our results indicated the SOC levels ranged from 10.46 g.kg(-1) to 37.60 g.kg(-1) and were significantly correlated with soil pH, landscape position, slope and elevation (p < 0.05). On a large scale, the combined effects of landscape position and elevation contributed to fluctuating SOC levels along the elevation gradient. SOC levels slightly, but significantly, decreased from base to summit. The difference of SOC levels along a 200 m elevation gradient exhibited statistical significance (p < 0.05). A slope range, from 0 to 42, was categorized into three groups, namely, 5 to 15, 15 to 30 and others. The slope range 15 to 30 had significantly greater SOC values than the other groups. These variables could all together explain approximately 40% of total variation in SOC, of which approximately 70% was attributable to soil pH, suggesting soil pH plays a key role in forming the spatial pattern of SOC levels in dry farming cropland soils of the mountainous region of Southwest China. The combined effect of landscape position and elevation could further explain 7.3% of SOC variation, which is more apparent than the effect of elevation alone

Multimodal salient object detection via adversarial learning with collaborative generator

Multimodal salient object detection(MSOD), which utilizes multimodal information (e.g., RGB image and thermal infrared or depth image) to detect common salient objects, has received much attention recently. Different modalities reflect different appearance properties of salient objects, some of which could contribute to improving the precision and/or recall of MSOD. To greatly improve both Precision and Recall by fully exploring multimodal data, in this work, we propose an effective adversarial learning framework based on a novel collaborative generator for accurate multimodal salient object detection. In particular, the collaborative generator consists of three generators (generator1, generator2 and generator3), which aim at decreasing the false positive and false negative of the generated saliency maps and improving F-measure of the final saliency maps respectively. Generator1 and generator2 contain two encoder–decoder networks for multimodal inputs, and we propose a new co-attention model to perform adaptive interactions between different modalities. Furthermore, we apply generator3 to integrate feature maps from generator1 and generator2 in a complementary way. Through adversarially learning the collaborative generator and discriminator, both Precision and Recall of the predicted maps are boosted with the complementary benefits of multimodal data. Extensive experiments on three RGBT datasets and six RGBD datasets show that our method performs quite well against state-of-the-art MSOD methods