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Deep learning (DL) has recently achieved outstanding performance in change detection of multitemporal images. However, most existing DL-based change detection methods still suffer from the problem of insufficient labeled training samples. To overcome this limitation, an unsupervised superpixel-guided self-supervised learning network (S3Net) is proposed for detecting changes occurred on the land surface. By performing principal component analysis on two input images, a triple-channel pseudocolor image containing the main information of both the images is first generated, which is used for superpixel segmentation to produce homogeneous image objects. Then, a Siamese network composing of two identical subnetworks with shared weight based on transfer learning is trained for pretext task in a self-supervised learning way, aiming to obtain multiscale object-level spatial feature difference images. On this basis, a high-quality difference image is generated by incorporating the pixel-level and object-level difference information using a simple weighted fusion strategy, which can be analyzed by thresholding to produce the final binary change map. The experimental results on four real-world datasets from different sensors show that the proposed approach can obtain superior performance in comparison to several state-of-the-art change detection methods, which further demonstrates its effectiveness and practicability. We make our data and code publicly available (https://github.com/OMEGA-RS/S3Net_CD). [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing including endmember extraction and abundance estimation has been investigated successively in recent years due to increasingly hyperspectral processing requirements. As one type of decision after solution paradigm, the multiobjective endmember estimation (MoEE) method is able to obtain a set of Pareto optimal solutions, thus providing a wealth of information to determine the most representative endmembers. In addition, multiobjective optimization methods also have the characteristics of flexible modeling, excellent global convergence, commendable adaptability, and so on. However, the evolutionary algorithms designed for this kind of method generally use little spatial–spectral information of the hyperspectral image. In this article, we delve into the memetic strategy by exploiting the topological structure of hyperspectral data in the high-dimensional space to establish a vertex-directed MoEE method, termed VD-MoEE. According to the frequently used linear mixture model, endmembers of hyperspectral images are generally distributed at the vertices of the hyperspectral data manifold. Therefore, we design a vertex-directed local search operator to guide the search direction of the individuals in evolutionary algorithms. Experimental results on synthetic and real datasets demonstrated that the proposed VD-MOEE is able to achieve appealing performance in terms of solution selection and accuracy in comparison with several classic and state-of-the-art endmember estimation methods. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) contain rich spectral signatures that reveal more image details and, thus, enable the detection of less noticeable changes on the ground. However, HSI-based change detection (CD) is susceptible to a large amount of irrelevant or noisy spectral and spatial information due to massive spectral bands. To address these issues, we propose a novel spectral and spatial attention network (S2AN) for HSI-based CD, which is capable to suppress CD-irrelevant spectral and spatial information via adaptive spectral and spatial attention mechanisms. S2AN takes as input the image patch from the difference map between two HSIs and outputs the status of change for the patch. Specifically, S2AN is composed of several repeated attention blocks, each of which contains the spectral attention (SpeA) module for directly calculating the attention score for each input channel, the Gaussian spatial attention (GSpaA) module that first constructs an adaptive Gaussian distribution and then samples it to derive the attention scores for each spatial position, and the convolutional feature extraction (CFE) module for extracting features from the attention-weighted input. It is worth mentioning that, in addition to the advantage of the attention, GSpaA also reduces the sensitivity of patch size for patch-based methods. To effectively train S2AN when facing insufficient labeled data, a semisupervised strategy that combines supervised and unsupervised methods to augment labeled training data is proposed. Experiments on several HSI datasets in comparison to existing methods show the superiority of S2AN. [ABSTRACT FROM AUTHOR]

Hyperspectral imagery is always composed of mixed pixels because of the limited spatial resolution of a sensor and the macroscopic/microscopic mixture of distinct substances. The linear mixing model (LMM) is proven to be simple and effective in extensive literature when the macroscopic mixture dominates the mixing process. But when the photons undergo multiple reflections before reaching the sensor, the LMM becomes invalid. In this circumstance, the bilinear mixture model (Bi-LMM), which considers secondary reflections with a bilinear term, is a viable alternative. However, the bilinear term in most existing Bi-LMMs is constructed based on the pre-estimated endmembers, and thus, most Bi-LMMs focus mainly on the abundance estimation. This may lead to inaccurate estimation of endmembers and abundances for a given hyperspectral image. In this article, we propose a multiobjective endmember extraction (Bi-MoEE) method within the bilinear mixture paradigm, which considers each secondary reflection as a virtual endmember. Then, Bi-MoEE selects real and virtual endmembers from an extended spectral library consisting of a standard spectral library and their virtual products. By imposing some intuitive constraints, the solution space is greatly reduced, and the multipoint crossover and restricted bit-flip mutation operators are specially designed. Finally, Bi-MoEE can efficiently obtain a set of tradeoff solutions by minimizing the unmixing residuals and the number of selected endmembers, and automatically determine the optimal solution with multiobjective decision-making techniques. Compared with some advanced endmember extraction methods, the proposed Bi-MoEE does not need to know the number of real endmembers. In addition, the time efficiency of Bi-MoEE is mainly related to the image size and the algorithmic parameters, and has little to do with the size of spectral library, thus facilitating the practical implementation of Bi-MoEE with regard to the oversized spectral library. The experiments on synthetic and real data sets demonstrated the excellent performance of Bi-MoEE. [ABSTRACT FROM AUTHOR]

The rapid development of remote sensing technology has enabled the acquisition of very high spatial resolution (VHR) multitemporal images in Earth observation. However, how to effectively exploit these existing data to accurately monitor land surface changes is still a challenging task. In this article, we propose an unsupervised scale-driven change detection (CD) framework for VHR images by jointly analyzing the spatial–spectral change information, which combines the advantages of deep feature learning and multiscale decision fusion. First, a well pretrained deep fully convolutional network (FCN) is used to automatically extract the deep spatial context information from the acquired images. Then, the uncertainty analysis incorporating the deep spatial feature and the image spectral feature is implemented to generate a pseudobinary change map. On this basis, it is easy to choose suitable samples to train an excellent support vector machine (SVM) classifier, thus detecting changes occurred on the ground. In addition, the multiscale superpixel segmentation technique is introduced to make full use of the spatial structural information, which takes an image-object as the basic analysis unit. Finally, a robust binary change map with high detection precision can be achieved by merging the CD results obtained at different scales. The impressive experimental results on four real data sets demonstrate the effectiveness and flexibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Effectively highlighting multiple changes in the earth surface from multi-temporal remote sensing images is a meaningful but challenging task. In order to reduce costs and ensure the performance, it is advisable to employ a semisupervised strategy to achieve this goal. As a discriminative joint classification task, semisupervised change detection aims to extract useful and discriminative features from a large amount of unlabeled data in addition to limited labeled samples. The discriminator of a well-trained generative adversarial network (GAN) is just right for this. Therefore, in this letter, we proposed a semisupervised GAN-based multiple change detection framework for multi-spectral images. First, the GAN is trained by all data without any prior information. Then, we combine two identical trained discriminators to construct a dual-pipeline joint classifier. Finally, the classifier is fine-tuned by a very small amount of labeled data to detect multiple changes. The superior performance of the proposed model over both real multi-spectral data sets demonstrates its robustness and effectiveness. [ABSTRACT FROM AUTHOR]

Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, making the direct comparison feasible. Our proposed framework contains a cGAN-based translation network that aims to translate the optical image with the SAR image as a target, and an approximation network that approximates the SAR image to the translated one by reducing their pixelwise difference. The two networks are updated alternately and when they are both trained well, the two translated and approximated images can be considered as homogeneous, from which the final change map can be acquired by direct comparison. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technology, how to accurately detect changes that have occurred on the land surface has been a critical task, particularly when images come from different satellite sensors. In this letter, we propose an unsupervised change detection method for heterogeneous synthetic aperture radar (SAR) and optical images based on the logarithmic transformation feature learning framework. First, the logarithmic transformation is applied to the SAR image that aims to achieve similar statistical distribution properties as the optical image. Then, high-level feature representations can be learned from the transformed image pair via joint feature extraction, which are used to select reliable samples for training a neural network classifier. When it is trained well, a robust change map can be obtained, thus identifying changed regions accurately. The experimental results on three real heterogeneous data sets demonstrate the effectiveness and superiority of the proposed method compared with other existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

With the rapid technological development of various satellite sensors, high-resolution remotely sensed imagery has been an important source of data for change detection in land cover transition. However, it is still a challenging problem to effectively exploit the available spectral information to highlight changes. In this paper, we present a novel change detection framework for high-resolution remote sensing images, which incorporates superpixel-based change feature extraction and hierarchical difference representation learning by neural networks. First, highly homogenous and compact image superpixels are generated using superpixel segmentation, which makes these image blocks adhere well to image boundaries. Second, the change features are extracted to represent the difference information using spectrum, texture, and spatial features between the corresponding superpixels. Third, motivated by the fact that deep neural network has the ability to learn from data sets that have few labeled data, we use it to learn the semantic difference between the changed and unchanged pixels. The labeled data can be selected from the bitemporal multispectral images via a preclassification map generated in advance. And then, a neural network is built to learn the difference and classify the uncertain samples into changed or unchanged ones. Finally, a robust and high-contrast change detection result can be obtained from the network. The experimental results on the real data sets demonstrate its effectiveness, feasibility, and superiority of the proposed technique. [ABSTRACT FROM PUBLISHER]