Open vocabulary semantic segmentation is a hot topic in research, focusing on segmenting and recognizing a diverse array of categories in varied environments, including those previously unknown, thereby holding significant practical value. Mainstream studies utilize the CLIP model for direct semantic segmentation (denoted as “forward methods”), which often struggles to represent underrepresented categories effectively. To address this issue, this paper introduces a novel approach Excluding the ImpossibLe Semantic Segmentation Network (ELSE-Net) based on reverse thinking. By excluding improbable categories, ELSE-Net narrows the selection range for forward methods, significantly reducing the risk of misclassification. In implementation, we initially draw on leading research to design the General Processing Block (GP-Block), which generates inclusion probabilities (the likelihood of belonging to a category) by using the CLIP model cooperated with a Mask Proposal Network (MPN). We then present the EXcluding the ImPossible Block (EXP-Block), which computes exclusion probabilities (the likelihood of not belonging to a category) through the CLIPN model and a custom-designed Reverse Retrieval Adapter (R2-Adapter). These exclusion probabilities are subsequently used to refine the inclusion probabilities, which are ultimately employed to annotate class-agnostic masks. Moreover, the core component of our EXP-Block is model-agnostic, enabling it to enhance the capabilities of existing frameworks. Experimental results from four benchmark datasets validate the effectiveness of ELSE-Net and underscore the seamless model-agnostic functionality of the EXP-Block.

Hyperspectral image (HSI) classification is a common task in remote sensing that often faces challenges due to limited samples and cross-domain discrepancies between training and test data. This particular problem is termed as HSI Cross-domain Few-Shot Classification (HSI-CFSC). To solve this problem, we propose a Self-motivated Ensemble Learning Model (SELM). Building upon source pre-trained representations, our end-to-end approach comprises a self-training paradigm to iteratively refine target representations independent of direct source supervision. Moreover, an ensemble classifier suite leveraging diverse decision boundaries is optimized to excavate comprehensive classification cues from limited labeled target data. The OA, AA and Kappa of SELM in UP, PC and Salinas data sets are respectively 86.55%, 82.27%, 82.10%, 98.07%, 94.20%, 97.30% and 91.33%, 94.96%, 90.37%, which achieve the state-of-art performance compared with other classical methods.

The distribution of remote sensing scene images often follows a long-tailed pattern, where there is an abundance of samples in a few dominant classes and a scarcity of samples in most other classes. This presents two major challenges when it comes to identifying this type of data: Head-Dominance: Models trained on such data tend to prioritize the dominant classes, overlooking the tail classes and resulting in poor performance when it comes to recognizing them. Tail-Interference: The presence of tail classes disrupts the learned representations for the head classes, acting as noise that negatively impacts the recognition accuracy of the head data. To address these challenges, we propose an innovative solution called the energy-based multiexpert (EME) model. The core concept behind this approach is to utilize energy-based discriminators (EDors) to separate the data into head and tail categories. Subsequently, we design multiple experts to classify the head and tail data separately, ensuring that the significant differences in data volume between these categories do not interfere with each other. Experimental results obtained by applying the EME model to three remote sensing datasets demonstrate its efficiency, outperforming current state-of-the-art (SOTA) methods. These findings underscore the effectiveness of our proposed approach in addressing the challenges posed by the long-tailed distribution in remote sensing scene images.