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.

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. Although AI techniques enable researchers to observe and understand the earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety critical. This article reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning (FL), uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors’ knowledge, this article is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the article to move this vibrant field of research forward.

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 ℓ ₁ -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.

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.