The objective of semantic change detection (SCD) is to discern intricate changes in land cover while simultaneously identifying their semantic categories. Prior research has shown that using multiple independent branches for the distinct tasks of change localization and semantic recognition is a reliable approach to solving the SCD problem. Nevertheless, conventional SCD architectures rely heavily on a high degree of consistency within the bitemporal feature space when modeling difference features, inevitably resulting in false positives or missed alerts within change areas. In this article, we introduce an SCD framework called the cross-differential semantic consistency network. Cross-difference semantic consistency (CdSC) is designed to mine deep discrepancies in bitemporal instance features while preserving their semantic consistency. Specifically, the 3-D cross-difference module, incorporating 3-D convolutions, explores the interaction of cross-temporal features, revealing inherent differences among various land features. Simultaneously, deep semantic representations are further utilized to enhance the local correlation of difference information, thereby improving the model’s discriminative capabilities within change regions. Incorporating principles from contrastive learning, a semantic co-alignment (SCA) loss is introduced to increase intra-class consistency and inter-class distinctiveness of dual-temporal semantic features, thereby addressing the challenges posed by semantic disparities. Extensive experiments on two SCD datasets demonstrate that CdSC outperforms other state-of-the-art SCD methods significantly in both qualitative and quantitative evaluations. The code and dataset are available at https://github.com/weiAI1996/CdSC .

Sea-land segmentation of remote sensing images is of great significance to the dynamic monitoring of coastlines. However, the types of objects in the coastal zone are complex, and their spectra, textures, shapes, and distribution features are different. Therefore, sea-land segmentation for various types of coastlines is still a challenging task. In this paper, a scale-adaptive semantic segmentation network, called SANet, is proposed for sea-land segmentation of remote sensing images. SANet has made two innovations on the basis of the classic encoder-decoder structure. First, to integrate the spectral, textural and semantic features of ground objects at different scales, we designed an adaptive multi-scale feature learning module (AML) to replace the conventional serial convolution operation. The AML module mainly contains a multi-scale feature extraction unit and an adaptive feature fusion unit. The former can capture the multi-scale detailed information and contextual semantic information of objects from an early stage, while the latter can adaptively fuse feature maps of different scales. Second, we adopted the squeeze-and-excitation module to bridge the corresponding layers of the codec so that SANet can selectively emphasize the features of the weak sea-land boundaries. Experiments on a set of Gaofen-1 remote sensing images demonstrated that SANet achieved more accurate segmentation results and obtained sharper boundaries than other methods for various natural and artificial coastlines.

Remote sensing shadow removal is an open issue. Previous studies focus on working in the spatial dimension, ignoring the potential of the Fourier dimension, while illumination degradation typically exists in the amplitude component. To address this limitation, our insight is a fresh dual-stage Fourier-based network (NeFour), which explores the best of both worlds between frequency and spatial information. In the frequency stage, we investigate the positive correlation between amplitude and brightness from channel and spatial statistics. Coupled with implicitly defined normalization, a controllable fitting amplitude transform map recreates the illumination. In the spatial stage, the inverted dark channel prior with 3D coordinates serves as modulation matrices that naturally reveal the spatial distribution of shadows, thus elegantly eliminating shadow remnants. With ingenious design, NeFour achieves nontrivial performance against state-of-the-art shadow removal methods in terms of both visual perception and quantitative evaluation. The code is publicly available at https://github.com/chi-kaichen/NeFour.

Airport runway extraction is important for daily maintenance of civil airports, precision strikes at military airports and safe landing of UAVs. However, different from the typical object extraction tasks, objects such as runways, taxiways, and roads share extremely similar attributes in terms of material, texture, and shape, making it challenging to differentiate between them. Besides, the gradients of some runway boundaries change slowly and are difficult to extract accurately. To address these problems, a Dual-field-of-view Context and Boundary Perception network (DCBP) is proposed, which can combine long-short-term contexts and boundary information of runways. Specifically, the Dual-field-of-view Context Aggregation (DCA) module can discover semantic representations from two perspectives by exploring the interaction between long-term and short-term contexts. Meanwhile, the detailed features learned from the high-resolution branch are utilized to guide the Boundary Perception (BP) module in learning the location of the runway boundaries. In addition to these, we provide the research community with a precisely labeled dataset, named Airport Runway Segmentation (ARS) dataset, to advance runway segmentation with remote sensing images. Extensive experiments on the benchmark demonstrated that DCBP achieved more accurate extraction results and obtained sharper boundaries on various airport runways than other methods. The code and dataset are available at https://github.com/weiAI1996/DCBP.