The high dimensions of hyperspectral remote-sensing images cause information redundancy and data processing complexity

thus leading to high computing workloads and low application accuracy. Therefore

before analyzing hyperspectral images

the high dimensions of hyperspectral data should be reduced. Classification is an important means of acquiring information

but existing image classification methods for identifying different ground objects at the pixel level and object level have low accuracies under the condition of strong noise interference to training samples. This interference decreases when similar objects with spectral and spatial characteristics are merged into large collections and classified according to the spectral and spatial characteristics of each collection. This paper proposes a double-population hybrid search strategy for dimension reduction based on differential evolution and particle swarm optimization with hybrid encoding. In this strategy

a support vector machine is adopted as a classifier with multiple-instance learning to improve the classification accuracy

reduce the dimension

and construct the classification model of encapsulation type. Experiments were conducted with AVIRIS images. Results show that the proposed method can obtain a classification accuracy of 96.03% for small training samples

0.62% higher than the best classification accuracy among similar hybrid encoding methods of classification. Under the strong noise interference of training samples

the proposed strategy utilizes the double-population hybrid search strategy

which is based on differential evolution and particle swarm optimization with hybrid encoding. The noise interference can be viewed as the specific form of "ambiguity" in the training package of multiple-instance learning. The proposed method can obtain high classification accuracy for small training samples and significantly alleviate strong noise interference.