Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.

Recently, convolutional neural networks (CNNs) have been widely applied to hyperspectral image (HSI) classification due to their detailed representation of features. Nevertheless, the current CNN-based HSI classification methods mainly follow a patch-based learning framework. These methods are nonglobal learning methods, which not only limit the use of global information but also require a high computational cost. In this letter, an image-based global learning framework is introduced to HSI classification. Based on this framework, we propose a dual-channel convolutional network (DCCN) for HSI classification to maximize the exploitation of the global and multiscale information of HSI. The experimental results conducted on two real hyperspectral datasets indicate that our method is superior to other related methods in terms of both efficiency and accuracy for HSI classification.

Water clarity, commonly measured as the Secchi disk depth ( $Z_{sd}$ ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including $QA{A}_{V5}$ , $QA{A}_{V6}$ , $QA{A}_{L09}$ and $QA{A}_{M14}$ , for the AHSI images with concurrent in situ measurements in four inland water bodies with a $Z_{sd}$ range of 0.3–4.5 m. The semi-analytical method with $QA{A}_{V5}$ can yield the most accurate $Z_{sd}$ predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated $Z_{sd}$ had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated $Z_{sd}$ were 0.22, 0.46, and 0.24 m for $Z_{sd}$ of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring.

A hyperspectral pushbroom sensor scans objects line-by-line using a detector array, and a cross-track illumination error (CTIE) exists in the imagery acquired in this way. When the illumination of the individual cells of the detector is not aligned well, or if some of the cells are degraded or old, the acquired images will exhibit non-uniform illumination in the cross-track direction. As additive Gaussian noise is found widely in hyperspectral images (HSIs), we develop a unified mathematical model that describes the image formation process corrupted by the CTIE and additive Gaussian noise. The cross-track illumination error produced by line-by-line scanning is replicated and modeled as an offset term with the equivalent values in the direction of flight. The main contribution of this study is the development of a hyperspectral image cross-track illumination correction (HyCIC) method, which corrects the cross-track illumination using column (along-track) mean compensation with total variation and sparsity regularizations, and attenuates the Gaussian noise by using a form of low-rank constraint. The effectiveness of the proposed method is illustrated using semi-real data and real HSIs. The performance of the proposed HyCIC is found to be better than other existing methods.

Water clarity ( $Z_{\mathrm {sd}}$ ) is a widely used quality indicator that can be estimated from remote sensing imagery. China’s newest generation advanced hyperspectral imager (AHSI) onboard the ZY1-02D satellite is expected to enable accurate water clarity retrieval for inland waters, since AHSI can provide abundant band choices, while its 30-m spatial resolution is advantageous for monitoring small inland water bodies. In this study, to retrieve $Z_{\mathrm {sd}}$ from the ZY1-02D imagery for inland waters with varying turbidities, we propose a classification-based, semianalytical method, in which the red/blue band ratio is employed to distinguish clear to moderately turbid water and highly turbid waters. Two quasi-analytical algorithms (QAAs), QAA _v5 and QAA _m14 , are used to estimate the total absorption coefficient [ $a(\lambda)$ ] and the backscattering coefficient [ $b_{b}(\lambda)$ ] for clear to moderately turbid water and highly turbid waters, respectively. The estimated $a(\lambda)$ and $b_{b}(\lambda)$ are utilized to obtain the diffuse attenuation coefficient $K_{d}$ , followed by the $Z_{\mathrm {sd}}$ calculations. Compared with 70 matchups of in situ measured $Z_{\mathrm {sd}}$ values (0–6.5 m), the ZY1-02D image-derived $Z_{\mathrm {sd}}$ achieved an $R^{2}$ of 0.98, with an average unbiased relative error and root mean square error of 29.1% and 0.52 m, respectively. In addition, the proposed method can yield $Z_{\mathrm {sd}}$ with higher accuracies than that of optimized empirical models. Therefore, the ZY1-02D AHSI imagery can retrieve reliable $Z_{\mathrm {sd}}$ for both clear (>3 m) and turbid waters (0–3.0 m), thereby serving as a useful satellite data source for monitoring the water clarity of large-scale inland water bodies.

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.