Remote sensing change detection (CD) aims to accurately reveal surface changes by comparing two temporally separated images of the same area. However, in complex environments, insufficient edge detail recognition and limited feature extraction often affect the accuracy of CD. For this purpose, we propose a novel method named the edge-object co-driven learning network (EOCLNet), which employs a combination of the Pyramid Vision Transformer (PVT) and the Fast Segment Anything Model (FastSAM) as parallel feature extractors to capture rich multilevel features. Specifically, it includes three key components which are the edge extraction module (EEM), the object revelation module (ORM), and the edge-object learning (EOL). EEM explicitly captures edge details by combining low-level spatial features with high-level semantic features, providing essential edge knowledge. ORM reveals changed objects by aggregating the highest two levels of semantic features, providing initial change guidance. EOL is designed to implicitly mine edge clues by establishing relationships between edges and changed objects across multiple levels, receiving outputs from both EEM and ORM. Furthermore, during the training process, the uncertainty from the previous level’s change map is utilized to guide the learning at the next level, thereby achieving a transition from uncertainty to certainty. The effectiveness of EOCLNet is validated on three public datasets, where it outperforms several state-of-the-art CD methods.

Change detection (CD) in remote sensing images aims at revealing earth surface changes between co-registered bitemporal images. A common way to reveal changed areas is to directly mix bitemporal features and generate CD results through supervised learning. However, a certain change usually corresponds to a real object in either of the two images, which exhibits coarse/fine shape in different scales. Therefore, a coarser-to-finer method called candidate-aware and change-guided network (CACG-Net) is proposed to effectively detect changes, where candidate objects are revealed and associated with interesting changes. Specifically, there are three key components. They are multistage change decoder (MCD), candidate-aware learning (CAL) and change guidance module (CGM). MCD reveals the most important changed objects in the coarse shape from the basic features extracted by the backbone (ResNet-18). To capture changes of interest, CAL is designed to select candidate objects in each temporal image, where a segmenter is utilized with variant change-losses. CGM intends to enrich the change details step-by-step through combining coarser change results and finer features, so that changed objects are gradually revealed in a coarser-to-finer way. Furthermore, deep supervision is employed throughout the layers of CACG-Net in the training procedure, which mitigates the learning difficulty in both deep and shallow layers. Test results on four popular datasets indicate that the proposed method outperforms several state-of-the-art CD algorithms in terms of accuracy and efficiency.

Change detection (CD) in remote sensing aims at identifying changes of specific categories from multitemporal images acquired at different moments of a given scene. Due to seasonal alteration and light variation, there are always pseudo-changes hard to be recognized. To this end, we propose a difference guidance learning way to mitigate the effects of pseudo-change, which benefits capturing more discriminative information and identifying real changes. Specifically, it combines difference information with fused features in a guidance way and generates discriminative features in multiple scales. Besides that, feature alignment is conducted in the highest stage to learn feature correlations between bitemporal images, which benefits identifying semantic changes by information exchange. Therefore, the proposed method is named feature alignment and difference guidance network (FADG-Net). Furthermore, a set of convolutional layers with different receptive field sizes is also utilized to capture spatial information across different scales and enhance texture features accordingly. Tested on three public CD datasets, the effectiveness of the proposed FADG-Net is verified, where pseudo-change problem is mitigated and our method is superior to other comparison methods.

Change detection (CD) of remote sensing images is to detect the change region by analyzing the difference between two bitemporal images. It is extensively used in land resource planning, natural hazards monitoring and other fields. In our study, we propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. In order to improve the generalization of networks, it computes the attention weights between any pixels between bitemporal images and uses them to engender more discriminating features. In order to improve the feature fusion and avoid gradient vanishing, multi-scale weighted variance map fusion strategy is proposed in the decoding stage. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.

Change detection (CD) plays an increasingly important role in earth observation and reveals surface changes according to multi-temporal images. Although deep learning-based CD methods work well for their excellent modeling ability, objects in different size and shape are generally processed by the same filter kernels in feature extraction, which leads to spatial blurring and degrades the CD performance. In this paper, a spatial adaptive and feature enhanced (SAFE) siamese network is proposed to tackle this problem, where the SAFE consists of a spatial-adaptive (SA) part and a feature-enhanced (FE) part. Specifically, pixel belonging to different objects possesses its own spatial knowledge, which is captured by a soft fusion of multi-scale difference images (DIs) called SA part. Changed and unchanged areas are strengthened or weakened by the FE, which combines object features with each DI accordingly. Moreover, since there are more unchanged pixels than changed pixels, a weight-pair is introduced to balance changed and unchanged objects in the training process. The experimental results verify that compared with four representative CD algorithms, our proposed method performs best on the Change Detection Dataset (CDD).