Remote sensing semantic segmentation must address both what the ground objects are within an image and where they are located. Consequently, segmentation models must ensure not only the semantic correctness of large-scale patches (low-frequency information) but also the precise localization of boundaries between patches (high-frequency information related to boundary components). However, most existing approaches rely heavily on discriminative learning, which excels at capturing low-frequency features, while overlooking its inherent limitations in learning high-frequency features for semantic segmentation. Recent studies have revealed that diffusion generative models excel at generating high-frequency details. Our theoretical analysis confirms that the diffusion denoising process significantly enhances the model's ability to learn high-frequency features; however, we also observe that these models exhibit insufficient semantic inference for low-frequency features when guided solely by the original image. Therefore, we integrate the strengths of both discriminative and generative learning, proposing the Integration of Discriminative and diffusion-based Generative learning for Boundary Refinement (IDGBR) framework. The framework first generates a coarse segmentation map using a discriminative backbone model. This map and the original image are fed into a conditioning guidance network to jointly learn a guidance representation subsequently leveraged by an iterative denoising diffusion process refining the coarse segmentation. Extensive experiments across five remote sensing semantic segmentation datasets (binary and multi-class segmentation) confirm our framework's capability of consistent boundary refinement for coarse results from diverse discriminative architectures. The source code is available at https://github.com/KeyanHu-git/IDGBR.

Due to the different acquisition conditions, large variations in the feature distributions of two temporal domains generally exist, known as temporal domain shift. The temporal domain shift is primarily influenced by coupled dual-factor: global style variations (such as illumination and weather conditions) and local style variations (such as the inherent phenological properties of land cover classes). In this article, we first formulate the temporal domain shift problem as an issue of dual-factor coupled interference on feature distributions in remote sensing (RS) community. To address this issue, we propose a semantic-guided style transfer (SGST) framework seamlessly integrating global feature alignment with local feature semantic matching. We use an adaptive segmentation model to provide pseudosegmentation maps and feed them into the style transfer model as semantic guidance. Under semantic guidance, a semantic-constrained style normalization (SCSN) module is designed to achieve style transfer at both global and local levels. Furthermore, a dual learning approach is introduced to make the style transfer model and the adaptive segmentation model promote each other. As a result, the style transfer model generates high-quality style-transferred images, and the adaptive segmentation model progressively predicts more accurate pseudosegmentation maps. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of both perceptual quality and quantitative performance.

With the development of deep learning, supervised learning methods perform well in remote sensing image (RSI) scene classification. However, supervised learning requires a huge number of annotated data for training. When labeled samples are not sufficient, the most common solution is to fine-tune the pretraining models using a large natural image data set (e.g., ImageNet). However, this learning paradigm is not a panacea, especially when the target RSIs (e.g., multispectral and hyperspectral data) have different imaging mechanisms from RGB natural images. To solve this problem, we introduce a new self-supervised learning (SSL) mechanism to obtain the high-performance pretraining model for RSI scene classification from large unlabeled data. Experiments on three commonly used RSI scene classification data sets demonstrated that this new learning paradigm outperforms the traditional dominant ImageNet pretrained model. Moreover, we analyze the impacts of several factors in SSL on RSI scene classification, including the choice of self-supervised signals, the domain difference between the source and target data sets, and the amount of pretraining data. The insights distilled from this work can help to foster the development of SSL in the remote sensing community. Since SSL could learn from unlabeled massive RSIs, which are extremely easy to obtain, it will be a promising way to alleviate dependence on labeled samples and thus efficiently solve many problems, such as global mapping.

Reducing the feature distribution shift caused by the factor of visual-environment changes, namely as VE-changes, is a hot issue in domain adaptation learning. However, in the semantic segmentation task of remote sensing imageries, besides VE-changes, the change of semantic-scenes (SS-changes) is another factor raising domain gap, which brings the label distribution shift. For example, although urban and rural share the same landcover label, there is still a gap in label distribution. If there is little relation that can be found in neither feature nor label space, forcibly adapting to a new domain could have a high risk of negative transfer. Hence, we propose a new Transitive Domain Adaptation method for Remote Sensing images (TDARS). Firstly, we introduce an intermediate domain to enlarge the relation between the given source and target domains. Secondly, we learn from primary and non-primary confident classes to increase the likelihood of transferring valuable information. As a result, TDARS enables the given source and target domains to be connected through the selected intermediate domain and performs effective knowledge transfer among all domains. The proposed method is evaluated on three domain adaptation datasets of remote sensing images. Extensive experiments show the approach can effectively handle the domain shift problem from remote sensing images compared to other state-of-the-art domain adaptation methods.

Remote sensing image (RSI) scene classification (RSISC) is a fundamental problem for understanding high-resolution RSIs. More recently, deep learning methods, especially convolutional neural networks (CNNs), and large data sets have greatly promoted the RSISC. However, deep learning methods rely heavily on the visual features extracted from the patches cropped from original RSIs, so the intraclass diversity and interclass similarity are two big challenges. To address these problems, in this letter, we propose a spatial information considered model to learn more discriminative features. By combining CNN and recurrent neural network, the proposed method can exploit both local and long-range spatial relation information to enhance the representational ability of the learned features. As the initial visual features of a single patch are transformed into higher-level features with spatial information, the proposed method achieves more accurate scene classification. Besides, we present an RSISC data set named as CSU-RSISC10 data set to preserve the spatial information between scenes in a new way of organization. Experiments demonstrate that the proposed method outperforms other three state-of-the-art methods in scene classification using CSU-RSISC10 data set.

For road extraction tasks in VHR satellite imagery, a deep neural network may perform well. But a network with certain reasoning ability as human will get a more satisfying result. To this end, we focus on how to effectively model the context information of the road and propose a well-designed spatial information inference structure (SIIS) which can add into any typical semantic segmentation network. The network with SIIS called SII-Net can not only learn the local visual characteristic of the road but also the global spatial structure information (such as the continuity and trend of the road). So, it can effectively solve the challenging occlusion problem in road detection and well preserve the continuity of the extracted road. The experimental results of two datasets show that the proposed method can improve the comprehensive performance of road extraction.

Though Hyperspectral Image (HSI) Classification has been extensively investigated over recent decades, it is still a challenge task especially when the number of labeled samples is extremely limited. In this paper, we overcome this challenge by using synthetic samples, and proposed a semi-supervised variational Generative Adversarial Networks(GANs) for this purpose. Compared to the conditional GAN which is recently used for generating samples for HSI classification, the proposed approach has two novel aspects. First, we extend the classic variational generative adversarial network to the semi-supervised context through an ensemble prediction technique. By this way, our model can be trained using limited labeled samples (only 5 samples per class) with a large number of unlabeled samples. Second, we adopt an encoder-decoder network to explicitly learn the relationship between the latent space and the real image space. This property enables our model producing diverse samples by simply varying some latent parameters, which is desirable for enriching the training dataset. We have shown that the proposed model can achieve better and robust performance for HSI classification compared to conditional GAN, especially when the labeled data is limited.