Nowadays, due to the difficult acquisition of true labels, a semisupervised neural network has shown great potential for change detection (CD) in remote sensing images. However, most of the traditional semisupervised neural network detection frameworks are complex to train and require additional structural analysis, along with a fixed structure, lacking universality. In this article, a semisupervised adaptive ladder network (SSALN) for remote sensing image CD is proposed, which enables dual-input label-incremental architecture searching with a concise and variable structure. First, SSALN is suitable for CD from two remote sensing images of any type with the characteristic of minimal label dependency and automatic network structure adjustment. The network can generate more reliable pseudolabels through continuous iterations to help limited real labels exploit implicit information, identify the most effective network, and form the ascending network structure optimization. Second, the acquisition of pseudolabels is the fusion of semisupervised and unsupervised CD approaches, which ensures the multiperspective information supplement. Multiple CD maps are fused to generate labels for the next iteration, making the predicting more reliable. Finally, both homogenous images and heterogenous images are tested with experiments. Even if the detection object is switched, it can be well adaptive and compatible without manual modification of the network. Experimental results demonstrate that the proposed method can promote the flow of label information through structure searching and self-circulation in the ascending network optimization; thus, it has outstanding performance on tasks of remote sensing image CD.

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this paper proposes self-guided autoencoders (SGAE) for unsupervised change detection in heterogeneous remote sensing images. Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudo-labeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudo-labeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method.

Recently, ensemble learning paradigm has shown great potential to achieve better prediction performance in the hyperspectral image classification. However, in the traditional methods, each classifier independently searches for the optimal spectral feature subspace in series and some important features are searched repeatedly, which leads to high computing redundancy and low effective utilization of features. In this paper, an evolutionary multi-task ensemble learning model (EMT EL) for hyperspectral image classification is designed. First, the model formulates the spectral feature subspaces generation into a multitask optimization problem to concurrently search for optimal feature subspaces for multiple classifiers, which would be capable to select more informative and representative feature subspaces effectively. Second, seeking the optimal feature subspace for one base classifier can assist in the optima-seeking process for some other base classifiers via sharing the useful features, which can accelerate converge towards the direction of the optimal feature subspace, avoid trapping in local optimal subspace and improve searching capability. Third, randomization-enhanced genetic operators are designed for effective and reasonable feature selection, which can facilitate the exchange of information and improve the joint searching efficiency of the feature subspace. Eventually, the quality of generated spectral feature subspaces for each base classifier is improved and the feature sharing can parse HSI data by knowing which spectral features are important. Experimental results demonstrate that the proposed method can generate the appropriate feature subspace for each base classifier, thus it has outstanding classification performance on the different hyperspectral datasets.

In the last few years, neural networks were introduced to change detection for a better understanding of remote sensing images. However, the designs of these neural networks were time-consuming processes of trial and error, which failed to account for their validity. Thus, a simple and efficient change detection method based on network architecture search in terms of the evolutionary algorithm is proposed to deal with SAR images change detection problems. In the proposed method, an efficient gene encoding is applied to represent the unpredictable optimal depth and the number of neurons in each hidden layer. Besides, a combinatorial evaluation strategy and a self-adaptive network solution selection are designed for effective and reasonable network architectures. What is more, a hidden layer random alignment crossover operator and a drawing lots mutation operator are designed for the enhancement of diversity of network architectures. Experimental results on a few SAR image data sets demonstrate that the proposed method can generate appropriate networks to solving SAR images change detection.