MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation (EO) applications, such as land use land cover mapping, environment monitoring, and sustainable development. Driven by rapid developments in Artificial Intelligence (AI), deep learning (DL) has emerged as the mainstream tool for semantic segmentation and achieved many breakthroughs in the field of remote sensing. However, the existing DL-based methods mainly focus on unimodal visual data while ignoring the rich multimodal information involved in the real world, usually demonstrating weak reliability and generlization. Inspired by the success of Vision Transformers and large language models, we propose a novel metadata-collaborative multimodal segmentation network (MetaSegNet) that applies vision-language representation learning for semantic segmentation of remote sensing images. Unlike the common model structure that only uses unimodal visual data, we extract the key characteristic (i.e. the climate zone) from freely available remote sensing image metadata and transfer it into knowledge-based text prompts via the generic ChatGPT. Then, we construct an image encoder, a text encoder and a crossmodal attention fusion subnetwork to extract the image and text feature and apply image-text interaction. Benefiting from such a design, the proposed MetaSegNet demonstrates superior generalization and achieves competitive accuracy with state-of-the-art semantic segmentation methods on the large-scale OpenEarthMap dataset (68.6% mIoU) and Potsdam dataset (93.3% mean F1 score) as well as LoveDA dataset (52.2% mIoU).

Transformer Meets Convolution: A Bilateral Awareness Net-work for Semantic Segmentation of Very Fine Resolution Ur-ban Scene Images

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, and urban planning, etc. However, the tremendous details contained in the VFR image severely limit the potential of the existing deep learning approaches. More seriously, the considerable variations in scale and appearance of objects further deteriorate the representational capacity of those se-mantic segmentation methods, leading to the confusion of adjacent objects. Addressing such is-sues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this manuscript, we pro-pose a bilateral awareness network (BANet) which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specif-ically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convo-lution operation. Besides, using the linear attention mechanism, a feature aggregation module (FAM) is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effective-ness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

A Novel Transformer based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images

The fully-convolutional network (FCN) with an encoder-decoder architecture has been the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an encoder to capture multi-level feature maps, which are incorporated into the final prediction by a decoder. As the context is crucial for precise segmentation, tremendous effort has been made to extract such information in an intelligent fashion, including employing dilated/atrous convolutions or inserting attention modules. However, these endeavours are all based on the FCN architecture with ResNet or other backbones, which cannot fully exploit the context from the theoretical concept. By contrast, we propose the Swin Transformer as the backbone to extract the context information and design a novel decoder of densely connected feature aggregation module (DCFAM) to restore the resolution and produce the segmentation map. The experimental results on two remotely sensed semantic segmentation datasets demonstrate the effectiveness of the proposed scheme.

SaNet: Scale-aware Neural Network for Semantic Labelling of Multiple Spatial Resolution Aerial Images

Assigning geospatial objects of aerial images with specific categories at the pixel level is a fundamental task in urban scene interpretation. Along with rapid developments in sensor technologies, aerial images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR aerial images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: 1) increased variation in the sizes of geospatial objects and 2) information and informative feature loss at coarse spatial resolutions. In this paper, we propose a novel scale-aware neural network (SaNet) for semantic labelling of MSR aerial images to address these two issues. SaNet deploys a densely connected feature network (DCFPN) module to capture high-quality multi-scale context, such as to address the scale variation issue and increase the quality of segmentation for both large and small objects simultaneously. A spatial feature recalibration (SFR) module is further incorporated into the network to learn complete semantic features with enhanced spatial relationships, where the effects of information and informative feature loss are addressed. The combination of DCFPN and SFR allows the proposed SaNet to learn scale-aware features from MSR aerial images. Extensive experiments undertaken on ISPRS semantic segmentation datasets demonstrated the outstanding accuracy of the proposed SaNet in cross-resolution segmentation, with an average OA of 83.4% on the Vaihingen dataset and an average F1 score of 80.4% on the Potsdam dataset, outperforming state-of-the-art deep learning approaches, including FPN (80.2% and 76.6%), PSPNet (79.8% and 76.2%) and Deeplabv3+ (80.8% and 76.1%) as well as DDCM-Net (81.7% and 77.6%) and EaNet (81.5% and 78.3%).