On May 15, 2021, China’s first Mars rover, the Zhurong rover successfully landed on the Utopian Planitia in the northern region of Mars. The multispectral camera (MSCam) on board the rover has captured multi-spectral images, which provide spatial and spectral information about in-situ observation targets and facilitate analysis of the types of materials on the Martian surface. However, frequent sandstorms on Mars are accompanied by dust deposition, and varying degrees of dust coverage have altered the original spectral characteristics of scientific detection targets, resulting in inaccurate material inversion. To address this issue, a novel in-situ dust cover index (IDCI) is proposed. The data-driven method is based on the spectral features of dust cover in the MSCam multispectral bands. It provides a wealth of information through a simple yet effective calculation that maximizes the discrimination between different categories of dust-impacted areas and estimates the degree of dust coverage. Experimental results obtained from 17 scientific observations by MSCam along the Zhurong rover’s routing path confirm the effectiveness of the proposed IDCI. It effectively distinguished dust-free areas from invalid areas (e.g., shadows), while reflecting the degree of dust coverage in the scene. The IDCI demonstrated its superior performance, operating up to three times faster than other reference methods. Additionally, it exhibited a notable advantage over other techniques, achieving a variance ration criterion (VRC) for target separation that was at least 5% higher. These results highlight the efficiency and effectiveness of the proposed IDCI, establishing it as a valuable tool for Martian surface analysis.

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.

In situ detection on Mars can provide detailed information on the planet’s topography and material composition while also validating the results obtained by orbiter probes. The laser-induced breakdown spectroscopy (LIBS) has emerged as a popular technology for Mars in situ exploration due to its fast response and high accuracy in identifying elements. The analysis of LIBS data obtained by different in situ scientific payloads onboard Mars rovers can help explain scientific problems related to about Martian geological genesis and history. However, it is essential to correct the data acquired by different instruments for joint analysis and to facilitate scientific discoveries due to variances in instrument specifications and data acquisition conditions. This article presents a novel cross-instrument spectral harmonization (CISH) approach that can eliminate differences in intensity and peak positions in LIBS spectra from different instruments. In particular, a peak position consistency correction (P2C2) method is proposed to correct cross-instrument peak position inconsistency by eliminating noise or irregular bumps presented in the LIBS spectra that may be incorrectly identified as characteristic peaks. The proposed CISH approach was validated using real Mars in situ LIBS data acquired by the chemistry and camera tool (ChemCam) and Mars surface composition detector (MarSCoDe). The experimental results demonstrate increased consistency in intensity and peak positions. Specifically, the average intensity difference decreased from 3.1287 to 2.1898, and the average peak position difference decreased from 0.1540 to 0.0335 nm. Meanwhile, the accuracy of inversion after consistency correction for the same calibration target (Norite) was also improved. The average root-mean-square error (RMSE) of eight oxides decreased from 5.18 to 3.37 by using a support vector machine (SVM) and from 18.80 to 7.07 using a partial least squares-submodel (PLS-SM). The proposed approach has the potential to establish a uniform benchmark for LIBS data acquired by different instruments at different times and locations, ensuring data consistency and comparability of identified material composition results.

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.

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.