Benefiting from the development of convolutional neural networks (CNNs), many excellent algorithms for object detection have been presented. Remote sensing object detection (RSOD) is a challenging task mainly due to: 1) complicated background of remote sensing images (RSIs) and 2) extremely imbalanced scale and sparsity distribution of remote sensing objects. Existing methods cannot effectively solve these problems with excellent detection accuracy and rapid speed. To address these issues, we propose an adaptive balanced network (ABNet) in this article. First, we design an enhanced effective channel attention (EECA) mechanism to improve the feature representation ability of the backbone, which can alleviate the obstacles of complex background on foreground objects. Then, to combine multiscale features adaptively in different channels and spatial positions, an adaptive feature pyramid network (AFPN) is designed to capture more discriminative features. Furthermore, considering that the original FPN ignores rich deep-level features, a context enhancement module (CEM) is proposed to exploit abundant semantic information for multiscale object detection. Experimental results on three public datasets demonstrate that our approach exhibits superior performance over baseline by only introducing less than 1.5M extra parameters.

Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.

Hyperspectral image super-resolution (SR) methods based on deep learning have achieved significant progress recently. However, previous methods lack the joint analysis between spectrum and horizontal or vertical direction. Besides, when both 2D and 3D convolution are in the network, the existing models cannot effectively combine the two. To address these issues, in this article, we propose a novel hyperspectral image SR method by exploring the relationship between 2D/3D convolution (ERCSR). Our method alternately employs 2D and 3D units to solve the problem of structural redundancy by sharing spatial information during reconstruction for existing model, which can enhance the learning ability of 2D spatial domain. Importantly, compared with the network using 3D units, i.e., 2D units are replaced by 3D units, it can not only reduce the size of the model but also improve the performance of the model. Furthermore, to exploit the spectrum fully, the split adjacent spatial and spectral convolution (SAEC) is designed to parallelly explore information between spectrum and horizontal or vertical direction in space. Experiments on widely used benchmark datasets demonstrate that the proposed approach outperforms state-of-the-art SR algorithms across different scales in terms of quantitative and qualitative analysis.

Band selection is considered as a direct and effective method to reduce redundancy, which is to select some informative and distinctive bands from the original hyperspectral image cube. Recently, many clustering-based band selection methods have been proposed, but most of them only take into account redundancy between bands, neglecting the amount of information in the subset of selected bands. Furthermore, these algorithms never consider the hyperspectral bands as ordered. Based on these two facts, we propose a novel approach for hyperspectral band selection via an adaptive subspace partition strategy (ASPS). The main contributions are as follows: 1) the ASPS is adopted to partition the hyperspectral image cube into multiple subcubes by maximizing the ratio of interclass distance to intraclass distance; 2) unlike previous methods, we estimate the band noise and select the band containing minimum noise (high-quality band) in each subcube to represent the whole subcube; and 3) adaptive subspace partition is viewed as a general framework and thus forms the variant version. Experimental results on three public datasets show that the proposed method achieves satisfactory results in both accuracy and efficiency than some state-of-the-art algorithms.

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the richness of information has been greatly improved. However, these bands lead to increasing complexity of data processing, and the redundancy of adjacent bands is large. Recently, although many band selection methods have been proposed, this task is rarely handled through the context information of the whole spectral bands. Moreover, the scholars mainly focus on the different numbers of selected bands to explain the influence by accuracy measures, neglecting how many bands to choose is appropriate. To tackle these issues, we propose a fast neighborhood grouping method for hyperspectral band selection (FNGBS). The hyperspectral image cube in space is partitioned into several groups using coarse-fine strategy. By doing so, it effectively mines the context information in a large spectrum range. Compared with most algorithms, the proposed method can obtain the most relevant and informative bands simultaneously as subset in accordance with two factors, such as local density and information entropy. In addition, our method can also automatically determine the minimum number of recommended bands by determinantal point process. Extensive experimental results on benchmark data sets demonstrate the proposed FNGBS achieves satisfactory performance against state-of-the-art algorithms.

Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three groups, and the current band is guided to study the potential knowledge. Under the action of alternative spectral fusion mechanism, the coarse SR image is super-resolved in band-by-band. In order to build model from a global perspective, an enhanced back-projection method via spectral angle constraint is developed in fine stage to learn the content of spatial-spectral consistency, dramatically improving the performance gain. Extensive experiments demonstrate the effectiveness of the proposed coarse stage and fine stage. Besides, our network produces state-of-the-art results against existing works in terms of spatial reconstruction and spectral fidelity. Our code is publicly available at https://github.com/qianngli/DualSR.