Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

Hyperspectral anomaly detection (HAD) aims to find small targets deviating from surroundings in an unsupervised manner. Recently, various deep models have been applied to HAD, such as autoencoder series and generative adversarial networks (GAN) series, which mainly use a proxy task, i.e., iteratively reconstructing low-frequency components (backgrounds) to separate anomalies (two-step paradigm). However, in such an unsupervised manner, most deep HAD model is trained and tested on the same image. Since the learned low-frequency background varies from image to image and the trained model cannot be directly transferred to unseen images. In this paper, the one-step detection paradigm is first proposed, where the model is optimized directly for the HAD task and can be transferred to unseen datasets. The one-step paradigm is optimized to identify the spectral deviation relationship according to the anomaly definition. Compared to learning the specific background distribution in the two-step paradigm, the spectral deviation relationship is universal for different images and guarantees transferability. Further, we instantiated the one-step paradigm as an unsupervised transferred direct detection (TDD) model. To train the TDD model in an unsupervised manner, an anomaly sample simulation strategy is proposed to generate numerous pairs of anomaly samples. A global self-attention module and a local self-attention module are designed to help the model focus on the “spectrally deviating” relationship. The TDD model was validated on six public datasets. The results show that TDD is superior to the recent two-step methods in detection and transferability aspects.

Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing a transferring model with cross-modality detection ability should be cost-effective and flexible to new earth observation sources and anomaly types. However, the current anomaly detectors aim to learn the certain background distribution, the trained model cannot be transferred to unseen images. Inspired by the fact that the deviation metric for score ranking is consistent and independent from the image distribution, this study exploits the learning target conversion from the varying background distribution to the consistent deviation metric. We theoretically prove that the large-margin condition in labeled samples ensures the transferring ability of learned deviation metric. To satisfy this condition, two large margin losses for pixel-level and feature-level deviation ranking are proposed respectively. Since the real anomalies are difficult to acquire, anomaly simulation strategies are designed to compute the model loss. With the large-margin learning for deviation metric, the trained model achieves cross-modality detection ability in five modalities—hyperspectral, visible light, synthetic aperture radar (SAR), infrared and low-light—in zero-shot manner.

Earth anomalies can locate valuable targets in an unsupervised manner for many defense and surveillance applications. Most models assign a continuous score at the pixel-level, resulting in object-agnostic results with higher false alarms than the instance-level results. However, since the anomaly objects contain a variety of categories and have a large intra-class variance, the current state-of-the-art query-based models designed for certain categories perform unsatisfactory when applying for the anomaly instances. The larger intra-class variance of anomalies makes the learning of general representation more difficult. To bridge this gap, we propose general adaptations guided by the pixel-level anomaly map for any query-based model, which adapts the model from learning certain category representation to learning anomaly-aware representation in different categories. The proposed adaptation firstly builds a separate branch to output the pixel-level anomaly map, where anomaly information is then extracted to guide the pixel embeddings and queries to focus on a variety of anomaly categories. Specially, the anomaly rank embeddings are devised to make the pixel embeddings aware of the anomaly rank order. The queries are dynamically selected from the anomaly candidates after aligning the anomaly map and pixel embeddings for better locating different anomalies. Finally, the selected queries dot product the anomaly-aware pixel embeddings to output the anomaly instances. The proposed adaptations are simple, general, and additive, which bring the average improvements of + 4.9 box AP and + 5.1 mask AP in infrared, SAR, and hyperspectral modalities.

Positive and unlabeled (PU) learning is aimed at building a binary classifier to distinguish the target from the background using only the known positive samples, which is an advanced solution for the hyperspectral target detection (HTD) task. However, when PU learning (PUL) meets complex hyperspectral scenarios, there are two main challenges: 1) How to estimate the class prior accurately? The class prior, i.e., the target proportion, is an important prior for PUL to learn the discriminant boundary, but it is difficult to estimate in hyperspectral imagery, due to the interclass spectral similarity and 2) How to remove redundancy and improve the discriminative features of the target? The diagnostic spectral feature extraction is important for the weakly supervised PUL models as it can help with separating the target from the background. In this article, to tackle these challenges, a robust PUL framework with key band selection (PU-KBS) is proposed, which is modeled as an end-to-end and class prior free PUL framework, where the accurate class prior and the most discriminative key band subset are jointly initialized and iteratively updated until reaching the optimal result by evolutionary search. Meanwhile, a deep PUL detector is introduced for guiding the subsequent search direction and discriminative deep feature extraction. The proposed PU-KBS framework was verified using different hyperspectral datasets, where accurate class prior estimation, diagnostic spectral characteristics, and robust detection results could be obtained simultaneously by the PU-KBS framework. Furthermore, the improvement in band selection interpretability and detection performance was proven experimentally.