Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral-spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral-spatial kernel improved residual network (A²S²K-ResNet) with spectral attention to capture discriminative spectral-spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral-spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A²S²K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated. The source code will be made available at https://github.com/suvojit-0x55aa/A2S2K-ResNet.

Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST), for texture classification. LETRIST is a histogram representation that explicitly encodes the joint information within an image across feature and scale spaces. The proposed representation is training-free, low-dimensional, yet discriminative and robust for texture description. It consists of the following major steps. First, a set of transform features is constructed to characterize local texture structures and their correlation by applying linear and non-linear operators on the extremum responses of directional Gaussian derivative filters in scale space. Established on the basis of steerable filters, the constructed transform features are exactly rotationally invariant as well as computationally efficient. Second, the scalar quantization via binary or multi-level thresholding is adopted to quantize these transform features into texture codes. Two quantization schemes are designed, both of which are robust to image rotation and illumination changes. Third, the cross-scale joint coding is explored to aggregate the discrete texture codes into a compact histogram representation, i.e., LETRIST. Experimental results on the Outex, CUReT, KTH-TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state-of-the-art approaches. Impressively, recognition rates of 100.00% and 99.00% have been achieved on the Outex and KTH-TIPS data sets, respectively. In addition, the noise robustness is evaluated on the Outex and CUReT data sets. The source code is publicly available at https://github.com/stc-cqupt/letrist .

Due to the existence of noise and spectral redundancies in hyperspectral images (HSIs), the band selection (BS) is highly required and can be achieved through the attention mechanism. However, existing BS methods fail to consider global interaction between the spectral information and spatial information in a nonlinear fashion. In this letter, we propose an end-to-end unsupervised dual-attention reconstruction network for BS ( DARecNet-BS ). The proposed network employs a dual-attention mechanism, i.e., position attention module (PAM) and channel attention module (CAM), to recalibrate the feature maps and subsequently uses a 3-D reconstruction network to restore the original HSI. This way, the long-range nonlinear contextual information in spectral and spatial directions is captured, and the informative band subset can be selected. Experiments are conducted on three well-known hyperspectral data sets, i.e., Indian Pines (IP), University of Pavia (UP), and Salinas (SA), to compare existing BS approaches, and the proposed DARecNet-BS can effectively select less redundant bands with comparable or better classification accuracy. The source code will be made publicly available at https://github.com/ucalyptus/DARecNet-BS .

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.

This letter introduces a novel grayscale-inversion and rotation invariant descriptor, called sorted local gradient pattern, for texture classification. First, we propose two complementary local gradient patterns (LGP), the center-to-ring LGP (LGP_CR) and the ring-to-ring LGP (LGP_RR), to encode rich gradient information present in a local neighborhood. Then, we propose to enhance LGP by encoding pixels' intensity information. This is achieved by sorting image pixels into two categories via a dominant intensity order measure, followed by extracting LGP features over the categorized pixels. As a result, local gradient information and global intensity order information are both encoded into our descriptor in a way that is robust to grayscale-inversion and rotation changes. Experiments on three texture databases demonstrate that the proposed descriptor achieves state-of-the-art classification results in the presence of linear and even nonlinear grayscale-inversion changes.