Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

The emergence of convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. Lack of training samples is the primary cause of low classification performance. Traditional CNN-based methods mainly use 2D CNN for feature extraction, which make interband correlations of HSIs underutilized. 3D CNN extracts the joint-spectral-spatial information representation, but it depends on a more complex model. Also, too deep or too shallow network cannot extract the image features well. To tackle these issues, we propose an HSI classification method based on 2D-3D CNN and multi-branch feature fusion. We first combine 2D CNN and 3D CNN to extract image features. Then, by means of the multi-branch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension. Finally, the fused features are passed into several fully connected layers and a softmax layer to obtain the classification results. In addition, our network model utilizes the state-of-the-art activation function Mish to further improve the classification performance. Our experimental results, conducted on four widely used HSI data sets, indicate that the proposed method achieves better performance than the existing alternatives.

This paper proposes a Cascaded Random Forest (CRF) method, which can improve the classification performance by means of combining two different enhancements into the Random Forest (RF) algorithm. In detail, on the one hand, a neighborhood rough sets based Hierarchical Random Subspace Method is designed for feature selection, which can improve the strength of base classifiers and increase the diversity between each two of the base classifiers; and on the other hand, Boosting is introduced into RF. As the minimization of the training error for updating the weights of samples in Boosting often leads to overfitting, we added out-of-bag error to update the sample weights in CRF. Different from the existing Boosting strategy that only one base classifier is generated at each iteration, the proposed CRF trains several base classifiers at each iteration. To evaluate the performance of our method, CRF is compared with other related RF methods and support vector machine on three benchmark hyperspectral datasets, and experimental results show that CRF can provide competitive solutions for hyperspectral image classification.

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

In this article, we propose a method for change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine (SVM) classification, which combined both the pixel-level method and the object-level method. Both pixel-based change features and object-based ones are extracted to improve the discriminability between the changed class and the unchanged class. At the pixel level, the change detection problem is formulated as a segmentation issue using level set evolution in the difference images. At the object level, potential training samples are selected from the segmentation results without manual intervention into the SVM classifier. Thereafter, the final changes are obtained by combining the pixel-based changes and the object-based changes. A chief advantage of our approach is being able to select appropriate samples for SVM classifier training. Furthermore, our proposed method helps improve the accuracy and the degree of automation. We systematically evaluate it with various Satellite Pour l’Observation de la Terre (SPOT) 5 images and aerial images. Experimental results demonstrate the accuracy of our proposed method.

Presently, to obtain a more accurate density map and crowd number, existing methods often count by combining training RGB images and depth images. However, these methods are not ideal for capturing and fusing complementary features in RGB-D. Therefore, to solve the above problems, we propose a collaborative cross-modal attention network named CCANet for accurate RGB-D crowd counting. CCANet is mainly composed of the collaborative cross-modal attention module (CCAM) and the collaborative cross-modal fusion module (CCFM). Specifically, CCAM focuses on adaptive, interleaved RGB-D information through channel and spatial cross-modal attentions to fully capture complementary features in different modes. CCFM can adaptively integrate these features by weighing the importance of the above complementary features. A large number of experiments on the ShanghaiTechRGBD and MICC benchmarks have proven the effectiveness of CCANet in RGB-D crowd counting. In addition, our CCANet is generally applicable to multimodal crowd counting and has achieved superior counting performance on the RGBT-CC benchmark.