With the development of convolutional neural network (CNN), many CNN-based object detection methods have made a remarkable success in very high-resolution (VHR) remote sensing images (RSIs). However, the standard convolution has a fixed receptive field, which makes it deficient in dynamic feature capture; complex backgrounds may also lead to the degradation of detection performance. Accordingly, this letter proposes a novel multiscale semantic guidance network (MSGN) to tackle these problems, wherein, based on the deformable convolution, an improved feature extraction backbone is proposed to capture features dynamically. Moreover, features from different layers are used to ensure the ability for detecting multiscale objects. Furthermore, a multilevel semantic guidance filtering subnetwork is proposed based on the designed backward semantic guidance filtering (BSGF) module, to suppress the complex backgrounds. Experimental results show that the proposed MSGN has stronger robustness and a better accuracy for multiscale object detection, compared with other reference methods.

In hyperspectral image classification, deep learning (DL) based on abundant training samples has demonstrated its significance in classification performance. However, due to the limitation of available samples and the imbalance/similarity of classes in small-sized datasets, data-driven DL algorithms can hardly extract representative and effective features for interclass classification, and the subtle diagnostic spectral features for intraclass classification are easily covered or lost in the iterative feature extraction (FE). The restricted FE of interclass/intraclass results in the accuracy reduction and performance limitation of refined classification. To mitigate these issues, an attention network with multiscale receptive fields (MRFs) is proposed, embedding an inversion subnet for relative water content retrieval (RWCR). In classification, the three critical parts in the proposed network, namely, MRFs, embedded subnet, and multiple-attention mechanism, are responsible for multiscale feature merging, relative water content (RWC) feature enhancement, and paying attention to bands, channels, and multiscale features, respectively. The ablation studies on small-sized datasets show the accuracy improvements of interclass and intraclass in refined classification, which verifies the effectiveness of critical parts for extracting representative features and taking RWC features as the diagnostic biochemical signature from unbalanced and similar classes. The comparison results with typical DL models demonstrate the superiority of the proposed network. Moreover, the competitive advantage of the proposed network is demonstrated in comparison with traditional and state-of-the-art HSI classification methods.

Hyperspectral image (HSI) classification methods based on deep learning (DL) algorithms have achieved significant improvements on abundant samples. However, due to the limitation of practically available samples, the difficulty of representative feature extraction from small-sized samples and the loss of subtle diagnostic features in DL iteration results in the accuracy reduction of interclass and intraclass in refined classification, respectively. To address these issues, a water retrieval embedded deep network is proposed in this paper. The relative water content retrieval (RWCR) of the proposed network is embedded as a subnet, which is responsible for extracting subtle diagnostic features of relative water content (RWC) to enhance the representation of features in classification. The experimental results verify the effectiveness of RWCR for improving the interclass and intraclass accuracy in refined classification. Moreover, the superiority of the proposed network is also demonstrated in comparison with state-of-the-art methods.

In hyperspectral image (HSI) classification, the distinction of similar classes has always been a focus of research. In this paper, a new classification module named class confusion merging (CCM) is proposed to improve the classification accuracy, especially for classes with the similar spectral feature. In CCM processing, the merging matrix is firstly constructed based on the confusion matrix to measure the similarity between different classes. Then similar classes are merged as big categories. Finally, for each big category, soft band selection is implemented based on the spectral difference of contained classes for reclassification. To evaluate the performance of CCM, real image experiments are conducted in comparison with no CCM module hyperspectral classification methods. The experiment results demonstrate that the CCM module can improve the classifier performance by providing higher classification accuracy.

Hyperspectral image (HSI) classification appro- aches achieve significant improvements with the proposal and application of deep learning algorithms. However, due to the end-to-end structure of the deep learning model, the exploration of intrinsic physical-chemical properties in HSI data is insufficient, which restricts the extraction of diagnostic features and impedes the improvement of intraclass classification performance. Moreover, the synergetic spectral-spatial feature extraction in deep learning model is limited owing to the difference between HSI spectral and spatial dimensions, which also hinders the refinement of performance. In order to mitigate these issues, an attention symbiotic neural network (ASNN) based on relative water content (RWC) retrieval (RWCR) is proposed for HSI refined classification in this article. ASNN is a multisupervised deep learning model that is able to extract spectral-spatial and biochemical features from the multilabel input data simultaneously. The augmented multilabel data, consisting of original spectral-spatial labels and RWC labels, are generated in the RWCR inversion model, which contains the proposed spectral index [red edge slope (RES)] calculation and the proposed adaptive grading algorithm. There are two critical parts in ASNN, soft band selection (SBS) module and dimensionality-varied feature extraction (DVFE) module, which are responsible for attention assignment and synergistic spectral-spatial feature extraction, respectively. The experimental results on real HSI data verify the effectiveness of RES, SBS, and DVFE in ablation studies. It is also demonstrated that ASNN has the capacity for improving intraclass and interclass accuracy in refined classification and providing a competitive advantage in comparison with several state-of-the-art methods.