Change detection, an essential application for high-resolution remote sensing (RS) images, aims to monitor and analyze changes in the land surface over time. Due to the rapid increase in the quantity of high-resolution RS data and the complexity of texture features, several quantitative deep learning-based methods have been proposed. These methods outperform traditional change detection (CD) methods by extracting deep features and combining spatial–temporal information. However, reasonable explanations for how deep features improve detection performance are still lacking. In our investigations, we found that modern Hopfield network (MHN) layers significantly enhance semantic understanding. In this article, we propose a deep supervision and feature retrieval network (Dsfer-Net) for bitemporal CD. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geographical information of the bitemporal images, we designed a feature retrieval module to extract difference features and leverage discriminative information in a deeply supervised manner. In addition, we observed that the deeply supervised feature retrieval (DSFR) module provides explainable evidence of the semantic understanding of the proposed network in its deep layers. Finally, our end-to-end network establishes a novel framework by aggregating retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Compared to the best-performing DSAMNet, Dsfer-Net demonstrates significant improvements, with$F1$scores increasing by 4.7%, 5.9%, and 2.3%. Furthermore, compared to our previous FrNet, Dsfer-Net also achieves noteworthy enhancements, with$F1$scores increasing by 2.0%, 1.4%, and 4.5% on three datasets. The code will be available online (https://github.com/ShizhenChang/Dsfer-Net).

In recent years, advanced research has focused on the direct learning and analysis of remote-sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image change captioning tasks, remote sensing change captioning aims to capture the most significant changes, irrespective of various influential factors such as illumination, seasonal effects, and complex land covers. In this study, we highlight the significance of accurately describing changes in remote sensing images and present a comparison of the change captioning task for natural and synthetic images and remote sensing images. To address the challenge of generating accurate captions, we propose an attentive changes-to-captions network, called Chg2Cap for short, for bi-temporal remote sensing images. The network comprises three main components: 1) a Siamese CNN-based feature extractor to collect high-level representations for each image pair; 2) an attentive encoder that includes a hierarchical self-attention block to locate change-related features and a residual block to generate the image embedding; and 3) a transformer-based caption generator to decode the relationship between the image embedding and the word embedding into a description. The proposed Chg2Cap network is evaluated on two representative remote sensing datasets, and a comprehensive experimental analysis is provided. The code and pre-trained models will be available online at https://github.com/ShizhenChang/Chg2Cap.

Recently, many collaborative representation-based (CR) algorithms have been proposed for hyperspectral anomaly detection. CR-based detectors approximate the image by a linear combination of background dictionaries and the coefficient matrix, and derive the detection map by utilizing recovery residuals. However, these CR-based detectors are often established on the premise of precise background features and strong image representation, which are very difficult to obtain. In addition, pursuing the coefficient matrix reinforced by the generall2-min is very time consuming. To address these issues, a nonnegative-constrained joint collaborative representation model is proposed in this paper for the hyperspectral anomaly detection task. To extract reliable samples, a union dictionary consisting of background and anomaly sub-dictionaries is designed, where the background sub-dictionary is obtained at the superpixel level and the anomaly sub-dictionary is extracted by the pre-detection process. And the coefficient matrix is jointly optimized by the Frobenius norm regularization with a nonnegative constraint and a sum-to-one constraint. After the optimization process, the abnormal information is finally derived by calculating the residuals that exclude the assumed background information. To conduct comparable experiments, the proposed nonnegative-constrained joint collaborative representation (NJCR) model and its kernel version (KNJCR) are tested in four HSI datasets and achieve superior results compared with other state-of-the-art detectors. The codes of the proposed method will be available online.

In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sources by learning a unified representation. In this way, comprehensive information from multiple views is shared and preserved for the generalization processes. As a special branch of temporal series hyperspectral image (HSI) processing, the anomalous change detection task focuses on detecting very small changes among different temporal images. However, when the volume of datasets is very large or the classes are relatively comprehensive, existing methods may fail to find those changes between the scenes, and end up with terrible detection results. In this paper, inspired by the sketched representation and multi-view subspace learning, a sketched multi-view subspace learning (SMSL) model is proposed for HSI anomalous change detection. The proposed model preserves major information from the image pairs and improves computational complexity by using a sketched representation matrix. Furthermore, the differences between scenes are extracted by utilizing the specific regularizer of the self-representation matrices. To evaluate the detection effectiveness of the proposed SMSL model, experiments are conducted on a benchmark hyperspectral remote sensing dataset and a natural hyperspectral dataset, and compared with other state-of-the-art approaches. The codes of the proposed method will be made available online1.

The positive unlabeled (PU) learning aims to train a binary classifier from a set of positive labeled samples and other unlabeled samples. Much research has been done on this special branch of weakly supervised classification problems. Since only part of the positive class is labeled, the classical PU model trains the classifier assuming the class-prior is known. However, the true class prior is usually difficult to obtain and must be learned from the given data, and the traditional methods may not work. In this paper, we formulate a convex formulation to jointly solve the class-prior unknown problem and train an accurate classifier with no need of any class-prior assumptions or additional negative samples. The class prior is estimated by pursuing the optimal solution of gradient thresholding and the classifier is simultaneously trained by performing empirical unbiased risk. The detailed derivation and theoretical analysis of the proposed model are outlined, and a comparison of our experiments with other representative methods prove the superiority of our method.

As one of the important applications of hyperspectral imagery (HSI) processing, the Mahalanobis distance-based detector in anomaly detection used to extract knowledge from the background and then calculate the Mahalanobis distance to obtain the detection result. Different from the previous work, a novel low-rank and sparse matrix decomposition (LRaSMD)-based dictionary reconstruction and anomaly extraction framework constructs the detector by a comprehensive combination of the low-rank and sparse matrix for hyperspectral anomaly detection. We use the LRaSMD to fully exploit the background and sparse information to construct the detector. For the low-rank part, we use atom usage probability to reconstruct the dictionary for follow-up collaborative representation (CR). For the sparse part, we calculate the Euclidean distance and get the result by using a ratio to add these two parts. The proposed algorithm was tested on three real-world HSI data sets and demonstrated outstanding detection performance when compared with other state-of-the-art detectors.

In this article, a new subspace selection-based discriminative forest (SSDF) method is proposed for the anomaly detection of hyperspectral remote sensing imagery. Most of the existing anomaly detection approaches construct a profile of background instances and then identify instances that do not conform to the background materials as anomalies. However, this type of method generally fails to avoid the background contamination caused by abnormal targets. In this case, we borrow from the concept of isolation and propose an isolation-based discriminative forest model which exploits subsampling rather than modeling the background instances. Furthermore, considering that the data volume of a hyperspectral image is usually huge, the proposed discriminative forest model explores a subspace selection process while splitting the leaf nodes of the binary trees to preserve those bands containing crucial abnormal target information and improve the reliability of the tree-splitting criterion. The proposed detector successfully integrates dimensionality reduction and the data-splitting technique to define pixels as anomaly or background. The extensive experimental results obtained with four hyperspectral data sets demonstrate that the proposed SSDF algorithm outperforms the other state-of-the-art algorithms and hence provides a new perspective for the anomaly detection of hyperspectral remote sensing imagery.

Considering the reconstruction error of autoencoder can reflect the characteristic of anomalies, this paper presents a novel hyperpsectral anomaly detection algorithm utilizes the residual of reconstruction error to estimate the anomalous information of the image. We firstly employ an adaptive dual concentric window to collect the training sets, and input pixels in the whole window and the outer window into the sparse autoencoders to learn the representations of the input, respectively. Then we use the representation parameters to reconstruct the test pixel and calculate the detection result by a deviation of reconstruction errors under two cases. The proposed sparse autoencoder-based anomaly detector experimental results have been conducted into the San Diego airport dataset and the Urban area dataset, the detection performances verified by the receiver operating characteristic curve and the area under curve show that the proposed method outperforms other representative detection methods.

Many hyperspectral anomaly detectors are designed based on the traditional Mahalanobis distance-based RX algorithm, which is usually considered as an inverse operation of the principal components analysis. Such detectors include the uniform target detector (UTD) algorithm, RX-UTD algorithm, and so on. However, the possibility of background statistical contamination caused by anomalies still exists. In order to alleviate this problem, in this paper, we propose a spectral matched filter (background-anomaly component projection and separation optimized filter) to minimize the average output energy of separate image components and the output values of the weighted background regular term for hyperspectral image anomaly detection, which could strengthen the separation between anomalies and backgrounds. By calculating the optimal solution to the background-anomaly component projection and separation function, we obtain the optimal projection, where we can effectively suppress the background while highlighting the anomalies. Proposed algorithm has the following research advantages: 1) it creates a novel collaborative component projection and robust background optimization function to separate the background and anomalies and 2) it analyzes the intrinsic statistical distribution of pixels and applies appropriate iterative shrinkage-thresholding algorithm to solve the ℓ1-min problem. Experiments were conducted on three real hyperspectral data sets. The detection results demonstrate that the proposed algorithm is superior to other state-of-the-art anomaly detection algorithms.