Hyperspectral image multi-class change detection (HSI-MCD) based on deep learning (DL) rely significantly on the number of labeled data. Due to the high cost of manually labeling for hyperspectral images (HSIs), obtaining a large amount of labeled samples is difficult. Moreover, for multi-class change detection (MCD) tasks, there is the phenomenon of semantic cross-coupling of changes due to complex change scenarios. To solve the above problems, a cross-domain few-shot learning method based on fractional domain information for HSI-MCD (FrCFSL) is proposed. Firstly, a spectral-spatial-fractional information extraction module is proposed, which can extract spectral-spatial-fractional domain joint feature. Thus, the module can obtain more comprehensive and discriminative representations of land cover categories, alleviating the phenomenon of semantic cross-coupling between classes. Afterward, a cross-domain fewshot learning strategy is introduced, where it learns task-relevant category discrimination meta-knowledge from a pair of richly labeled very high-resolution optical images (VHRIs) dataset and transfers it to the bitemporal HSIs dataset. Thus, the model can achieve better MCD performance with a small number of labeled samples. Finally, to mitigate the domain distribution differences between VHRIs data and HSIs data, a topological structure alignment module is proposed to align the intrinsic topological relationships between land cover categories, thus narrowing the gap between the two domain distributions. Through experiments conducted on three HSI-MCD datasets and comparative analysis with six state-of-the-art methods, the validity and stability of the proposed method are indicated.

Hyperspectral image change detection (HSI-CD) possesses strong capabilities in exploring subtle changes in land cover. Due to sensor noise and imaging conditions, different semantic land covers in the same spatial location may exhibit similar spectral characteristics, leading to pseudoinvariant phenomena (identification of changed areas as unchanged areas) and causing a higher rate of false negatives in the model. Existing methods primarily focus on obtaining auxiliary discriminative information from spatial correlations or temporal dependencies. However, the frequency domain, which possesses rich global gradient distribution information, is often overlooked. The fractional Fourier transform (FrFT) is an extension of the Fourier transform (FT), representing a temporal-frequency local transformation suitable for processing nonstationary signals. Furthermore, multiorder fractional Fourier domains provide more observable domains for change discrimination. In this work, the application of FrFT is extended to the field of HSI-CD, and an adaptive weighted metric learning network based on fractional domain decoupling (FrFTML) is proposed. Specifically, the fractional domain decoupling (FrDD) module transforms the original HSI into multiorder FrFT domains and extracts their rich spatial-frequency mixed information, effectively suppressing noise while enhancing the representation of subtle differences. In addition, an adaptive weighted metric learning (AWML) framework is designed to merge multiorder fractional Fourier domain information in an adaptively weighted fusion manner. It introduces deep metric learning to explore the distances between samples of different categories that have relatively high similarity, so as to guide the direction of adaptive weighted fusion. Finally, the differential mask attention (DMA) module is designed to explore global contextual differences between bitemporal HSIs, obtaining change features with well-represented differences. Some experiments conducted on three public datasets indicate that FrFTML outperforms other state-of-the-art methods. Furthermore, the proposed method exhibits superiority in dealing with land cover that may lead to pseudoinvariant phenomena (identification of changed areas as unchanged areas).

Change detection has consistently been a prominent direction in the field of remote sensing. As for high resolution remote sensing images (HRRSI), despite the notable achievements of change detection models, the majority of their impressive performance stems from their large scale architecture or computational requirements. To strike a balance between efficiency and efficacy, a lightweight change detection method based on transformer and feature interaction (LiFTNet) has been proposed. LiFTNet utilizes an efficient backbone, EfficientNet-B4, which is a lightweight network architecture. To fully utilize the information in features with limited model parameters, a multi scale feature interaction module (MSFI) is proposed to aggregate the shallow features and the deep features. As the network has a shallow depth, the semantic information contained in the features is incomplete. To enhance the extraction of semantic information with minimal increases in computational overhead, a lightweight semantic transformer is adopted in the model. A series of experiments indicate the superior performance of LiFTNet over other state-of-the-art (SOTA) methods, showing both efficiency and effectiveness.

Change detection is a prominent research direction in the field of remote sensing image processing. However, most current change detection methods focus solely on detecting changes without being able to differentiate the types of changes, such as “appear” or “disappear” of objects. Accurate detection of change types is of great significance in guiding decision-making processes. To address this issue, this article introduces the object fine-grained change detection (OFCD) task and proposes a method based on frequency decoupling interaction (FDINet). Specifically, in order to enhance the model’s ability to detect change types and improve its robustness to temporal information, a temporal exchange framework is designed. Additionally, to better capture spatial–temporal correlation in bi-temporal features, a wavelet interaction module (WIM) is proposed. This module utilizes wavelet transform for frequency decoupling, separating features into different components based on their frequency magnitudes. Then the module applies different interaction methods according to the characteristics of these frequency components. Finally, to aggregate complementary information from different-scale feature maps and enhance the representational capabilities of the extracted features, a feature aggregation and upsampling module (FAUM) is adopted. A series of experiments show the superiority of FDINet over most state-of-the-art methods, achieving good results on three different datasets.

In recent years, deep learning has demonstrated its transformative potential in the field of hyperspectral image (HSI) processing but is notoriously data-hungry. However, wanting to obtain a large number of labels is labor-intensive and time-consuming. To reduce the dependence of the model on the label samples while maintaining high detection accuracy, a hyperspectral image change detection algorithm based on active learning strategy (ALCD) is proposed. First, the active learning strategy is employed to select high-value labeled samples from the test set as additional training data, gradually enhancing the model’s detection performance. Second, the self-attention module MOAT is introduced to enable effective interaction of local information during the feature extraction process and enhance the network’s feature expression capability. Then, the feature interaction and the mixing block are used to blend the features of the bitemporal images, so that the feature distribution of the bitemporal images is more similar, which is conducive to subsequent feature extraction and classification. Experiments on two HIS datasets show that the proposed method can obtain better change detection results than the four comparison algorithms.

Erosion gullies are a prominent manifestation of soil erosion. And timely and accurate acquisition of relevant data about erosion gullies plays a crucial role in their management and control. Currently, there is a deficiency in automation within the majority of erosion gully detection methods. The post-classification comparison method using semantic segmentation techniques and the direct change detection method often struggle to ensure high accuracy. Therefore, a end to end change detection method based on deep supervised and feature interaction (DSFNet) is proposed for erosion gullies in this paper. To achieve accurate localization of erosion gully semantic information, DSFNet employs a deep supervision strategy to constrain the semantics of erosion gullies. Furthermore, in order to extract representative features related to erosion gullies and improve the detection accuracy of the model, a feature interaction and upsampling module (IUModule) is employed. Experimental results show that DSFNet exhibits better performance on erosion gully dataset.

With the development of remote sensing technology, high-resolution (HR) remote sensing optical images have gradually become the main source of change detection data. Albeit, the change detection for HR remote sensing images still faces challenges: 1) in complex scenes, a region contains a large amount of semantic information, which makes it difficult to accurately locate the boundaries between different semantics in the feature maps and 2) due to the inability to maintain consistent conditions such as light, weather, and other factors when acquiring bitemporal images, confounding factors such as the style of bitemporal data that are not related to change detection can cause detection difficulties. Therefore, a change detection method based on feature interaction and multitask learning (FMCD) is proposed in this article. To improve the ability to detect changes in complex scenes, FMCD models the context information of features through a multilevel feature interaction module, so as to obtain representative features, and to improve the sensitivity of the model to changes, the interaction between two temporal features is realized through the mix attention block (MAB). In addition, to eliminate the influence of weather and other factors, FMCD adopts a multitask learning strategy, takes domain adaptation as an auxiliary task, and maps the features of bitemporal images to the same space through the feature relationship adaptation module (FRAM) and feature distribution adaptation module (FDAM). Experiments on three datasets show that the proposed method is superior to other state-of-the-art methods.

1Change detection has always been a hot research area in the field of hyperspectral image (HSI) processing. However, in the current change detection methods, most of them need to train a large number of labeled data to extract representative features. In this paper, a hyperspectral change detection method based on multi-scale three-dimensional (3D) convolution autoencoder network (M3CAN) is proposed. Firstly, the multi-scale 3D convolution block is adopted in the autoencoder which can extract effective spectral-spatial joint features of HSIs. Then, the autoencoder is pre-trained to obtain the trained encoder as the feature extractor. Finally, the feature maps of the bi-temporal data are obtained by the encoder and then sent to the Softmax classifier to obtain the final change detection result. In this paper, unsupervised training of autoencoder is combined with supervised training of classifier. Therefore, only a small amount of data is needed to complete the training, which avoids the difficulty of requiring many labeled training data. Experiments show that the proposed method has good results on two datasets.