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The Hough forest method is an effective method for object detection in ground-shot images that has received increasing research attention. However, this method lacks the ability to detect objects with arbitrary orientations. This largely constrains the method from being used in detecting geospatial objects from remotely sensed (RS) images since geospatial objects can have many different orientations. In order to achieve rotation invariance and compensate the associated loss of discriminative power, this paper presents a novel color-enhanced rotation-invariant Hough forest (CRIHF) method for detecting geospatial objects in RS images. In our method, we propose to train a Pose-Estimation-based Rotation-invariant Texton Forest (PE-RTF) which first uses dominant gradient orientations to align local image patches. The orientations are then jointly used with coordinates in Hough voting to detect object position. In order to increase discriminative power, Texton Forest is used in codebook generation. Moreover, theoretically sound color-invariant gradients are employed. By rotating split functions rather than image patches in the RTF and sparsely accumulating Hough votes on grid points, computational times can be reduced by two orders of magnitude. The evaluation of the CRIHF method on a data set containing 525 airplanes and a second data set containing 68 residential buildings shows that our method is rotation invariant and robust. The detector achieves around 90% recall rate on both data sets. Experiments also show that our method is noise resistant and can achieve a decent detection performance at a high level (30%) of “salt and pepper” impulsive noise.