Multitemporal change detection (CD) plays a crucial role in the remote sensing application field. In recent years, supervised deep learning methods have shown excellent performance in detecting changes in very-high-resolution (VHR) images. However, these methods require a large number of labeled samples for training, making the process time-consuming and labor-intensive. Unsupervised approaches are more attractive in practical applications since they can produce a CD map without relying on any ground reference or prior knowledge. In this article, we propose a novel unsupervised CD approach, named transformer-based multivariate alteration detection (trans-MAD). It utilizes a pre-detection strategy that combines the compressed change vector analysis and the iteratively reweighted multivariate alteration detection (IR-MAD) to generate reliable pseudotraining samples. More accurate and robust CD results can be achieved by leveraging the IR-MAD to detect insignificant changes and by incorporating the transformer-based attention mechanism to model the difference or similarity between two distant pixels in an image. The proposed trans-MAD approach was validated on two VHR bitemporal satellite remote sensing datasets, and the obtained experimental results demonstrated its superiority comparing with the state-of-the-art unsupervised CD methods.

With the increasing availability and resolution of satellite sensor data, multispectral (MS) and panchromatic (PAN) images are the most popular data that are used in remote sensing among applications. This article proposes a novel cross-resolution hidden layer feature fusion (CRHFF) approach for joint classification of multiresolution MS and PAN images. In particular, shallow spectral and spatial features at a global scale are first extracted from an MS image. Then, deep cross-resolution hidden layer features extracted from MS and PAN are fused from patches at a local scale according to an autoencoder (AE)-like deep network. Finally, the selected multiresolution hidden layer features are classified in a supervised manner. By taking advantage of integrated shallow-to-deep and global-to-local features from the high-resolution MS and PAN images, the cross-resolution latent information can be extracted and fused in order to better model imaged objects from the multimodal representation and finally increase the classification accuracy. Experimental results obtained on three real multiresolution datasets covering complex urban scenarios confirm the effectiveness of the proposed approach in terms of higher accuracy and robustness with respect to literature methods.

Among various multimodal remote sensing data, the pairing of multispectral (MS) and panchromatic (PAN) images is widely used in remote sensing applications. This article proposes a novel global collaborative fusion network (GCFnet) for joint classification of MS and PAN images. In particular, a global patch-free classification scheme based on an encoder-decoder deep learning (DL) network is developed to exploit context dependencies in the image. The proposed GCFnet is designed based on a novel collaborative fusion architecture, which mainly contains three parts: 1) two shallow-to-deep feature fusion branches related to individual MS and PAN images; 2) a multiscale cross-modal feature fusion branch of the two images, where an adaptive loss weighted fusion strategy is designed to calculate the total loss of two individual and the cross-modal branches; 3) a probability weighted decision fusion strategy for the fusion of the classification results of three branches to further improve the classification performance. Experimental results obtained on three real datasets covering complex urban scenarios confirm the effectiveness of the proposed GCFnet in terms of higher accuracy and robustness compared to existing methods. By utilizing both sampled and non-sampled position data in the feature extraction process, the proposed GCFnet can achieve excellent performance even in a small sample-size case. The codes will be available from the website: https://github.com/SicongLiuRS/GCFnet.

Landform classification and mapping of the Martian surface using Mars orbiter images can provide an important reference for landing site selection and rovers’ traversability evaluation in Mars exploration. Moreover, specific Martian landforms are closely associated with the evidences of water-related activities and Martian life, thus have crucial research importance. This article proposes a novel superpixel-guided multiview feature fusion network (MarsMapNet) for efficient mapping of the Martian landforms. In particular, the proposed MarsMapNet first generates the superpixel-level segments from Mars orbiter images by considering local morphological homogeneity of landforms. Then, a multiview feature extraction and fusion (MVF) network is developed, where abstract convolutional features are extracted based on scene-level patches, and multitextures are extracted based on local landform from shallow-to-deep feature learning. After the network being trained on scene-level samples and guided by the superpixel segmentation, Martian landforms can be correctly classified in an efficient way, whose mapping time cost sharply decreased when compared to the reference methods. The proposed MarsMapNet has been validated on three real landing sites from several Mars missions (i.e., the Jezero Crater, the Southern Utopia Planitia, and the Oxia Planum) by using the Mars Reconnaissance Orbiter’s Context Camera (CTX) images. Qualitative and quantitative analyses on the obtained experimental results confirm the effectiveness and efficiency of the proposed MarsMapNet when compared with the state-of-the-art (SOTA) methods, demonstrating its potential for supporting a Martian global landform mapping in the future.

The Tianwen-1 mission, China's first interplanetary endeavor and Mars Mission, touched the surface of the Red Planet on May 15, 2021. With the successful landing of the Zhurong rover on the southern Utopian Plain of the Mars, the Mars Surface Composition Detector (MarSCoDe) on board of the rover has started to analyze the material composition of the Martian surface by using the Laser Induced Breakdown Spectroscopy (LIBS). However, changes in instrument temperature and external environment during operation will cause spectral drift in LIBS data, leading to inaccurate material composition inversion results due to shifts in elemental characteristic peak positions. To address this problem, an adaptive spectral drift correction (ASDC) approach is proposed. By considering the distribution of characteristic peaks and drift differences between different elements, the proposed ASDC method corrects the spectral drift in LIBS science data according to an adaptive spectral segmentation strategy. Experimental results obtained on 36 real LIBS science data that acquired over 19 exploration activities confirmed the effectiveness of the proposed approach in terms of lower average drift values, by comparing with two reference methods. Taking the on-board calibration data as a reference, the average spectral drift values of the LIBS science data before and after applying ASDC decreased from 0.1012 nm to 0.0179 nm, 0.0926 nm to 0.0158 nm, and 0.1697 nm to 0.0103 nm in three LIBS channels, respectively. Furthermore, the resulting decrease in the standard deviation and root mean square error of the average drift values also proved the robustness of proposed method.

The Martian surface landforms are highly related to the safe landing and traversability of Mars rovers. Furthermore, landforms associated with the presence of water/ice, minerals and biosignatures can provide valuable insights for Mars exploration missions, particularly in relation to the selection of landing or sample collection sites. The small number of Martian landform datasets and the scarcity of labelable landform samples over Mars make the precise mapping of Martian landforms a challenging task. In this article, we propose a stepwise deep feature transfer (SDFT) model for the mapping of Martian landforms with a small number of labeled samples. The SDFT model comprises two transfer steps. In the first transfer step, a deep learning model trained on a large public source dataset from Earth is transferred to a medium sized public dataset from Mars. This transfer is conducted through a standard pre-training and fine-tuning procedure utilizing a linear classifier. In the second transfer step, the model is further transferred to a small number of target datasets on Mars through a pre-training and fine-tuning procedure with a cosine distance classifier. The stepwise training technique mitigates the challenges associated with varying datasets and small training samples. The proposed SDFT model has been validated on two self-built sample sets using images from the Mars Reconnaissance Orbiter’s Context Camera (CTX). It has also been employed for landform mapping in two local regions with small samples to evaluate its effectiveness in comparison with existing state-of-the-art methods.