With the improvement of spatial resolution, the conveyed information of remote sensing images has become increasingly intricate. The semantic content of pixels within the same object exhibits considerable variability, while the semantic content of pixels between different objects exhibits significant overlap. However, most existing approaches focus solely on establishing the internal consistency of objects by aggregating global or multi-scale contextual information without adequately considering the orientation and spatially detailed features of the target. Moreover, these methods often overlook the potential of edge information in achieving accurate edge positioning. These defects will adversely affect the accuracy of segmentation. In this paper, we present an Edge Information Guided Network (EIGNet), which leverages edge information to guide the aggregation of rich contextual information for semantic segmentation to improve the segmentation accuracy of high-resolution remote sensing images. Specifically, an Orientation Convolution Module (OCM) is proposed to construct a spatial detail branch for acquiring precise edge information and spatial detail information. To effectively guide the aggregation of spatial detail features and semantic features, we propose a spatial-semantic feature aggregation module (SSFAM). Moreover, to enhance the extraction of long-range dependencies of irregular objects, we propose the Orientation Atrous Convolution Module (OACM), which facilitates the extraction of multi-phase long-range dependencies of objects. The ISPRS Vaihingen and Potsdam datasets are employed to validate the efficacy of the proposed methodology and draw comparisons with various state-of-the-art techniques. The experimental results demonstrate that the proposed method offers distinct advantages.

As spatial resolution increases, the information conveyed by remote sensing images becomes more and more complex. Large-scale variation and highly discrete distribution of objects greatly increase the challenge of the semantic segmentation task for remote sensing images. Mainstream approaches usually use implicit attention mechanisms or Transformer modules to achieve global context for good results. However, these approaches fail to explicitly extract intra-object consistency and inter-object saliency features leading to unclear boundaries and incomplete structures. In this paper, we propose a Category-Guided Global-Local Feature Interaction Network (CGGLNet), which utilizes category information to guide the modeling of global contextual information. To better acquire global information, we proposed a Category-Guided Supervised Transformer module (CGSTM). This module guides the modeling of global contextual information by estimating the potential class information of pixels so that features of the same class are more aggregated and those of different classes are more easily distinguished. To enhance the representation of local detailed features of multi-scale objects, we designed the Adaptive Local Feature Extraction Module (ALFEM). By parallel connection of the CGSTM and the ALFEM, our network can extract rich global and local context information contained in the image. Meanwhile, the designed Feature Refinement Segmentation Head (FRSH) helps to reduce the semantic difference between deep and shallow features and realizes the full integration of different levels of information. Extensive ablation and comparison experiments on two public remote sensing datasets (ISPRS Vaihingen dataset and ISPRS Potsdam dataset) indicate that our proposed CGGLNet achieves superior performance compared to the state-of-the-art methods.

Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many methods have been continuously proposed in recent years. However, it is still challenging to achieve high accuracy classification in applications. One of the main reasons is the lack of labeled data. Due to the limitation of spatial resolution, manual labeling of HSI data is time-consuming and costly, so it is difficult to obtain a large amount of labeled data. In such a situation, many researchers turn their attention to the study of HSI classification with small samples. Focusing on this topic, this paper provides a systematic review of the research progress in recent years. Specifically, this paper contains three aspects. First, considering that the taxonomy used in previous review articles is not well-developed and confuses the reader, we propose a novel taxonomy based on the form of data utilization. This taxonomy provides a more accurate and comprehensive framework for categorizing the various approaches. Then, using the proposed taxonomy as a guideline, we analyze and summarize the existing methods, especially the latest research results (both deep and non-deep models) that were not included in the previous reviews, so that readers can understand the latest progress more clearly. Finally, we conduct several sets of experiments and present our opinions on current problems and future directions.

Joint spectral and spatial information processing is an effective means to improve the classification accuracy of hyperspectral remote sensing images. The Markov random field (MRF) is a powerful tool for integrating spectral and contextual information into the classification framework. However, the shallow structure of the MRF cannot fully exploit the information of hyperspectral imagery. In this article, a cascaded MRF model is proposed to combine the benefit of the MRF and cascaded model. The model consists mainly of two phases. In the first phase, the predicted probability vector generated by the support vector machine classifier and the MRF model is combined. Then, the combined feature vector is concatenated with the original spectral feature to generate an enhanced feature vector with more discriminating power. In the subsequent stage, the enhanced feature vector is used as the input of the next level of the cascaded MRF model. Experiments based on three widely used hyperspectral data show that the proposed method has state-of-the-art performance.

Hyperspectral imagery (HSI) classification is a rapidly growing and highly active research area in the field of hyperspectral community. The method that combines both spatial and spectral information for hyperspectral image classification has made a great advance. The focus of spectral-spatial classification is how to extract discriminating features and how to combine the spectral and spatial information effectively. In this paper, we propose a new spectral-spatial method to solve these two problems. The first part is a series of support vector machines (SVMs) that are cascaded to form the enhanced features, where the predicted information of the preceding layer can provide correction information for the subsequent layer, and then a new superpixel segmentation method is adopted to introduce multi-scale spatial information, which can generate multi-scale superpixels at one time and is as accurate as the state-of-the-art methods. The final label is determined by majority voting of different scales.

Shadow detection is a basic task of remote sensing image analysis, but it is often seriously disturbed by vegetation, water bodies, and black objects. It is observed that vegetation and dark objects often show a dark look in visible bands but brighter in the near-infrared (NIR), and is also noticed that the reflection of inland water bodies in the green band is stronger than that in the blue band. Taking advantage of these physical properties and combining them with the bluish and dark appearance of shadows, we propose a simple but effective shadow detection method for multispectral remote sensing images. These physical properties are used to create transformation models that suppress features such as vegetation, water bodies, etc., but at the same time enhance shadows. Then, we transform the shadow representation into a color space to generate candidate shadows using dominant color components. To separate shadows from the others, we propose two indexes, the normalized Color Difference Composite Index (CDCI) and Color Purity Index (CPI), and fuse them to achieve shadows and their confidence. The experimental results indicate that the proposed method can effectively detect the shadows in multispectral images and outperforms the state-of-the-art approaches.