Image-level weakly-supervised land-cover classification (WSLC) relies on image labels to free land-cover classification networks from expensive pixel-level annotations. Existing weakly-supervised methods commonly generate class activation maps (CAMs) from a trained classification network to produce pixel-level pseudo labels for training land-cover classification networks. However, CAMs struggle to completely and accurately capture land-covers in remote sensing images due to the lack of precise localization information in weak supervision. To alleviate semantic uncertainty in CAMs, this paper proposes an Expanding-Separating (ES) scheme to generate high-quality CAMs of land-covers. The ES scheme expands activation areas in CAMs by lifting activation values in inactivated regions and suppresses CAM noises by decreasing values in confusing regions. Experimental results on the DeepGlobe dataset demonstrate that the proposed ES scheme significantly improves the quality of CAMs and benefits the training of land-cover classification, achieving state-of-the-art performance.

This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene under-standing. We observe that the search space constructed by current methods for proposal feature grouping and instance point sampling often introduces massive noise to instance detection and mesh reconstruction. Accordingly, we develop AncLearn to generate anchors that dynamically fit instance surfaces to (i) unmix noise and target-related features for offering reliable proposals at the detection stage, and (ii) reduce outliers in object point sampling for directly providing well-structured geometry priors without segmentation during reconstruction. We embed AncLearn into a reconstruction-from-detection learning system (AncRec) to generate high-quality semantic scene models in a purely instance-oriented manner. Experiments conducted on the challenging ScanNetv2 dataset demonstrate that our shape anchor-based method consistently achieves state-of-the-art performance in terms of 3D object detection, layout estimation, and shape reconstruction. The code will be available at https://github.com/Geo-Tell/AncRec.

The inference of topological principles is a key problem in structured reconstruction. We observe that wrongly predicted topological relationships are often incurred by the lack of holistic geometry clues in low-level features. Inspired by the fact that massive signals can be compactly described with frequency analysis, we experimentally explore the efficiency and tendency of learning structure geometry in the frequency domain. Accordingly, we propose a frequency-domain feature learning strategy (F-Learn) to fuse scattered geometric fragments holistically for topology-intact structure reasoning. Benefiting from the parsimonious design, the F-Learn strategy can be easily deployed into a deep reconstructor with a lightweight model modification. Experiments demonstrate that the F-Learn strategy can effectively introduce structure awareness into geometric primitive detection and topology inference, bringing significant performance improvement to final structured reconstruction. Code and pre-trained models are available at https://github.com/Geo-Tell/F-Learn.

This article presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: Feature mixing in edge classification and side mixing during fusing side predictions. The CATS consists of two modules: A novel tracing loss that performs feature unmixing by tracing boundaries for better side edge learning, and a context-aware fusion block that tackles the side mixing by aggregating the complementary merits of learned side edges. Experiments demonstrate that the proposed CATS can be integrated into modern deep edge detectors to improve localization accuracy. With the vanilla VGG16 backbone, in terms of BSDS500 dataset, our CATS improves the F-measure (ODS) of the RCF and BDCN deep edge detectors by 12 and 6 percent, respectively when evaluating without using the morphological non-maximal suppression scheme for edge detection.

Convolutional neural network (CNN)-based feature fusion of RGB and auxiliary remote sensing data is known to enable improved semantic segmentation. However, such fusion is challengeable because of the substantial variance in data characteristics and quality (e.g., data uncertainties and misalignment) between two modality data. In this article, we propose a unified gather-to-guide network (G2GNet) for remote sensing semantic segmentation of RGB and auxiliary data. The key aspect of the proposed architecture is a novel gather-to-guide module (G2GM) that consists of a feature gatherer and a feature guider. The feature gatherer generates a set of cross-modal descriptors by absorbing the complementary merits of RGB and auxiliary modality data. The feature guider calibrates the RGB feature response by using the channel-wise guide weights extracted from the cross-modal descriptors. In this way, the G2GM can perform RGB feature calibration with different modality data in a gather-to-guide fashion, thus preserving the informative features while suppressing redundant and noisy information. Extensive experiments conducted on two benchmark datasets show that the proposed G2GNet is robust to data uncertainties while also improving the semantic segmentation performance of RGB and auxiliary remote sensing data.